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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Statistical Computing</h1><div class="u-tcGrayDark">9,700 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Statistical Computing</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Statistical_Computing">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Statistical_Computing/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Statistical_Computing/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Statistical_Computing/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Statistical_Computing">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20107825" data-work_id="20107825" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20107825/Confidence_intervals_for_sensitivity_indices_using_reduced_basis_metamodels">Confidence intervals for sensitivity indices using reduced-basis metamodels</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the original model by a much faster to run... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20107825" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the original model by a much faster to run code. In this paper, we are interested in the information loss induced by the approximation on the estimation of sensitivity indices. We present a method to provide a robust error assessment, hence enabling significant time savings without sacrifice on precision and rigourousness. We illustrate our method with an experiment where computation time is divided by a factor of nearly 6. We also give directions on tuning some of the parameters used in our estimation algorithms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20107825" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="cf759e19e1e08b6989e3e1e35e9968e3" rel="nofollow" data-download="{"attachment_id":41011897,"asset_id":20107825,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41011897/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="41146071" href="https://independent.academia.edu/AlexandreJanon">Alexandre Janon</a><script data-card-contents-for-user="41146071" type="text/json">{"id":41146071,"first_name":"Alexandre","last_name":"Janon","domain_name":"independent","page_name":"AlexandreJanon","display_name":"Alexandre Janon","profile_url":"https://independent.academia.edu/AlexandreJanon?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_20107825 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="20107825"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 20107825, container: ".js-paper-rank-work_20107825", }); 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The reduced-basis approach provides a way to replace the original model by a much faster to run code. In this paper, we are interested in the information loss induced by the approximation on the estimation of sensitivity indices. We present a method to provide a robust error assessment, hence enabling significant time savings without sacrifice on precision and rigourousness. We illustrate our method with an experiment where computation time is divided by a factor of nearly 6. We also give directions on tuning some of the parameters used in our estimation algorithms.","downloadable_attachments":[{"id":41011897,"asset_id":20107825,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":41146071,"first_name":"Alexandre","last_name":"Janon","domain_name":"independent","page_name":"AlexandreJanon","display_name":"Alexandre Janon","profile_url":"https://independent.academia.edu/AlexandreJanon?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":247452,"name":"Information Loss","url":"https://www.academia.edu/Documents/in/Information_Loss?f_ri=1351","nofollow":true},{"id":470296,"name":"Global sensitivity analysis","url":"https://www.academia.edu/Documents/in/Global_sensitivity_analysis?f_ri=1351","nofollow":true},{"id":497452,"name":"Numerical Model","url":"https://www.academia.edu/Documents/in/Numerical_Model?f_ri=1351","nofollow":true},{"id":1587858,"name":"Confidence Interval","url":"https://www.academia.edu/Documents/in/Confidence_Interval?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9957287 coauthored" data-work_id="9957287" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9957287/Classification_of_hyperspectral_data_from_urban_areas_based_on_extended_morphological_profiles">Classification of hyperspectral data from urban areas based on extended morphological profiles</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9957287" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9957287" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c1eefe3dc40e4fb1bff9d21a34f3b183" rel="nofollow" data-download="{"attachment_id":36105083,"asset_id":9957287,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36105083/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="24193489" href="https://hi.academia.edu/J%C3%B3nAtliBenediktsson">Jón Atli Benediktsson</a><script data-card-contents-for-user="24193489" type="text/json">{"id":24193489,"first_name":"Jón Atli","last_name":"Benediktsson","domain_name":"hi","page_name":"JónAtliBenediktsson","display_name":"Jón Atli Benediktsson","profile_url":"https://hi.academia.edu/J%C3%B3nAtliBenediktsson?f_ri=1351","photo":"https://0.academia-photos.com/24193489/6530851/7384463/s65_j_n_atli.benediktsson.jpg_oh_6b7b45651424280310b707f4a3840755_oe_55443aaa___gda___1429137018_5a4d9d762f5e62ceca5e42fe2f47fff0"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-9957287">+1</span><div class="hidden js-additional-users-9957287"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://grenoble-inp.academia.edu/JocelynChanussot">Jocelyn Chanussot</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-9957287'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-9957287').html(); 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A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.","downloadable_attachments":[{"id":36105083,"asset_id":9957287,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":24193489,"first_name":"Jón Atli","last_name":"Benediktsson","domain_name":"hi","page_name":"JónAtliBenediktsson","display_name":"Jón Atli Benediktsson","profile_url":"https://hi.academia.edu/J%C3%B3nAtliBenediktsson?f_ri=1351","photo":"https://0.academia-photos.com/24193489/6530851/7384463/s65_j_n_atli.benediktsson.jpg_oh_6b7b45651424280310b707f4a3840755_oe_55443aaa___gda___1429137018_5a4d9d762f5e62ceca5e42fe2f47fff0"},{"id":33057657,"first_name":"Jocelyn","last_name":"Chanussot","domain_name":"grenoble-inp","page_name":"JocelynChanussot","display_name":"Jocelyn Chanussot","profile_url":"https://grenoble-inp.academia.edu/JocelynChanussot?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":409,"name":"Geophysics","url":"https://www.academia.edu/Documents/in/Geophysics?f_ri=1351","nofollow":true},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":5069,"name":"Principal Component Analysis","url":"https://www.academia.edu/Documents/in/Principal_Component_Analysis?f_ri=1351","nofollow":true},{"id":8053,"name":"Independent Component Analysis","url":"https://www.academia.edu/Documents/in/Independent_Component_Analysis?f_ri=1351"},{"id":10866,"name":"Morphology","url":"https://www.academia.edu/Documents/in/Morphology?f_ri=1351"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=1351"},{"id":16904,"name":"Hyperspectral remote sensing","url":"https://www.academia.edu/Documents/in/Hyperspectral_remote_sensing?f_ri=1351"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=1351"},{"id":36276,"name":"Mathematical Morphology","url":"https://www.academia.edu/Documents/in/Mathematical_Morphology?f_ri=1351"},{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=1351"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=1351"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering?f_ri=1351"},{"id":285356,"name":"Hyperspectral Imaging","url":"https://www.academia.edu/Documents/in/Hyperspectral_Imaging?f_ri=1351"},{"id":302692,"name":"Layout","url":"https://www.academia.edu/Documents/in/Layout?f_ri=1351"},{"id":309086,"name":"High Resolution","url":"https://www.academia.edu/Documents/in/High_Resolution?f_ri=1351"},{"id":599945,"name":"Principal Components","url":"https://www.academia.edu/Documents/in/Principal_Components?f_ri=1351"},{"id":604200,"name":"Spatial resolution","url":"https://www.academia.edu/Documents/in/Spatial_resolution?f_ri=1351"},{"id":920861,"name":"Hyperspectral Data","url":"https://www.academia.edu/Documents/in/Hyperspectral_Data?f_ri=1351"},{"id":972674,"name":"Hyperspectral Imagery","url":"https://www.academia.edu/Documents/in/Hyperspectral_Imagery?f_ri=1351"},{"id":1127444,"name":"High Spatial Resolution","url":"https://www.academia.edu/Documents/in/High_Spatial_Resolution?f_ri=1351"},{"id":1130224,"name":"Hyperspectral Sensors","url":"https://www.academia.edu/Documents/in/Hyperspectral_Sensors?f_ri=1351"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=1351"},{"id":1984794,"name":"Urban Area","url":"https://www.academia.edu/Documents/in/Urban_Area?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67085960" data-work_id="67085960" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67085960/Self_Optimizing_Neural_Networks">Self-Optimizing Neural Networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The paper is concentrated on two essential problems: neural networks topology optimization and weights parameters computation that are often solved separately. This paper describes new solution of solving both selected problems together.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67085960" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The paper is concentrated on two essential problems: neural networks topology optimization and weights parameters computation that are often solved separately. This paper describes new solution of solving both selected problems together. According to proposed methodology a special kind of multilayer ontogenic neural networks called Self-Optimizing Neural Networks (SONNs) can simultaneously develop its structure for given training data and compute</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67085960" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ee4c0f0d423c66b52ddc21373d0f0146" rel="nofollow" data-download="{"attachment_id":78036115,"asset_id":67085960,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78036115/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="87832349" href="https://agh.academia.edu/AdrianHorzyk">Adrian Horzyk</a><script data-card-contents-for-user="87832349" type="text/json">{"id":87832349,"first_name":"Adrian","last_name":"Horzyk","domain_name":"agh","page_name":"AdrianHorzyk","display_name":"Adrian Horzyk","profile_url":"https://agh.academia.edu/AdrianHorzyk?f_ri=1351","photo":"https://0.academia-photos.com/87832349/57696603/45917913/s65_adrian.horzyk.png"}</script></span></span></li><li class="js-paper-rank-work_67085960 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67085960"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67085960, container: ".js-paper-rank-work_67085960", }); 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$(".js-view-count[data-work-id=67085960]").text(description); $(".js-view-count-work_67085960").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67085960").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67085960"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="20097" rel="nofollow" href="https://www.academia.edu/Documents/in/Topology_Optimization">Topology Optimization</a>, <script data-card-contents-for-ri="20097" type="text/json">{"id":20097,"name":"Topology Optimization","url":"https://www.academia.edu/Documents/in/Topology_Optimization?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26066" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Network">Neural Network</a><script data-card-contents-for-ri="26066" type="text/json">{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67085960]'), work: {"id":67085960,"title":"Self-Optimizing Neural Networks","created_at":"2022-01-04T07:48:15.434-08:00","url":"https://www.academia.edu/67085960/Self_Optimizing_Neural_Networks?f_ri=1351","dom_id":"work_67085960","summary":"The paper is concentrated on two essential problems: neural networks topology optimization and weights parameters computation that are often solved separately. This paper describes new solution of solving both selected problems together. According to proposed methodology a special kind of multilayer ontogenic neural networks called Self-Optimizing Neural Networks (SONNs) can simultaneously develop its structure for given training data and compute","downloadable_attachments":[{"id":78036115,"asset_id":67085960,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":87832349,"first_name":"Adrian","last_name":"Horzyk","domain_name":"agh","page_name":"AdrianHorzyk","display_name":"Adrian Horzyk","profile_url":"https://agh.academia.edu/AdrianHorzyk?f_ri=1351","photo":"https://0.academia-photos.com/87832349/57696603/45917913/s65_adrian.horzyk.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":20097,"name":"Topology Optimization","url":"https://www.academia.edu/Documents/in/Topology_Optimization?f_ri=1351","nofollow":true},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=1351","nofollow":true},{"id":29073,"name":"Network optimization","url":"https://www.academia.edu/Documents/in/Network_optimization?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82484990" data-work_id="82484990" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82484990/Three_parameter_stochastic_lognormal_diffusion_model_statistical_computation_and_simulating_annealing_application_to_real_case">Three-parameter stochastic lognormal diffusion model: statistical computation and simulating annealing – application to real case</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we propose a new study of a stochastic lognormal diffusion process (SLDP), with three parameters, which can be considered as an extension of the bi-parametric lognormal process with the addition of a threshold parameter.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82484990" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we propose a new study of a stochastic lognormal diffusion process (SLDP), with three parameters, which can be considered as an extension of the bi-parametric lognormal process with the addition of a threshold parameter. From the Kolmogorov equation, we obtain the probability density function and the moments of this process. The statistical inference of the parameter is studied by considering discrete sampling of the sample paths of the model and then using the maximum likelihood (ML) method. The estimation of the threshold parameter requires the solution of a nonlinear equation. To do so, we propose two methods: the classical Newton-Raphson (NR) method and one based on simulated annealing (SA). This methodology is applied to an example with simulated data corresponding to the process with known parameters. From this, we obtain the estimators of the parameters by both methods (NR and SA). Finally, the methodology studied is applied to a real case concerning the mean age of males in Spain at the date of their first wedding.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82484990" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d87f218eed303f4ac1e253b37b55ae7b" rel="nofollow" data-download="{"attachment_id":88179324,"asset_id":82484990,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/88179324/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="81938612" href="https://www-ufg.academia.edu/EdgarRamos">Edgar Ramos</a><script data-card-contents-for-user="81938612" type="text/json">{"id":81938612,"first_name":"Edgar","last_name":"Ramos","domain_name":"www-ufg","page_name":"EdgarRamos","display_name":"Edgar Ramos","profile_url":"https://www-ufg.academia.edu/EdgarRamos?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_82484990 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82484990"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82484990, container: ".js-paper-rank-work_82484990", }); 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$(".js-view-count[data-work-id=82484990]").text(description); $(".js-view-count-work_82484990").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82484990").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82484990"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6421" rel="nofollow" href="https://www.academia.edu/Documents/in/Simulated_Annealing">Simulated Annealing</a><script data-card-contents-for-ri="6421" type="text/json">{"id":6421,"name":"Simulated Annealing","url":"https://www.academia.edu/Documents/in/Simulated_Annealing?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82484990]'), work: {"id":82484990,"title":"Three-parameter stochastic lognormal diffusion model: statistical computation and simulating annealing – application to real case","created_at":"2022-07-01T22:51:59.296-07:00","url":"https://www.academia.edu/82484990/Three_parameter_stochastic_lognormal_diffusion_model_statistical_computation_and_simulating_annealing_application_to_real_case?f_ri=1351","dom_id":"work_82484990","summary":"In this paper, we propose a new study of a stochastic lognormal diffusion process (SLDP), with three parameters, which can be considered as an extension of the bi-parametric lognormal process with the addition of a threshold parameter. From the Kolmogorov equation, we obtain the probability density function and the moments of this process. The statistical inference of the parameter is studied by considering discrete sampling of the sample paths of the model and then using the maximum likelihood (ML) method. The estimation of the threshold parameter requires the solution of a nonlinear equation. To do so, we propose two methods: the classical Newton-Raphson (NR) method and one based on simulated annealing (SA). This methodology is applied to an example with simulated data corresponding to the process with known parameters. From this, we obtain the estimators of the parameters by both methods (NR and SA). Finally, the methodology studied is applied to a real case concerning the mean age of males in Spain at the date of their first wedding.","downloadable_attachments":[{"id":88179324,"asset_id":82484990,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":81938612,"first_name":"Edgar","last_name":"Ramos","domain_name":"www-ufg","page_name":"EdgarRamos","display_name":"Edgar Ramos","profile_url":"https://www-ufg.academia.edu/EdgarRamos?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":6421,"name":"Simulated Annealing","url":"https://www.academia.edu/Documents/in/Simulated_Annealing?f_ri=1351","nofollow":true},{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=1351"},{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference?f_ri=1351"},{"id":87364,"name":"Maximum Likelihood","url":"https://www.academia.edu/Documents/in/Maximum_Likelihood?f_ri=1351"},{"id":872399,"name":"Probability Density Function","url":"https://www.academia.edu/Documents/in/Probability_Density_Function?f_ri=1351"},{"id":982614,"name":"Diffusion Model","url":"https://www.academia.edu/Documents/in/Diffusion_Model?f_ri=1351"},{"id":3615220,"name":"Newton Raphson","url":"https://www.academia.edu/Documents/in/Newton_Raphson?f_ri=1351"},{"id":4044434,"name":"nonlinear equation","url":"https://www.academia.edu/Documents/in/nonlinear_equation?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9179977" data-work_id="9179977" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9179977/Power_grid_analysis_based_on_a_macro_circuit_model">Power grid analysis based on a macro circuit model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Analysis and design of on-chip power grids are complex problems. A typical grid consists of hundreds of millions of transistors that act as current consumers. Typical algorithms for power grid analysis or power grid automatic design,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9179977" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Analysis and design of on-chip power grids are complex problems. A typical grid consists of hundreds of millions of transistors that act as current consumers. Typical algorithms for power grid analysis or power grid automatic design, model the current consumers, namely the CMOS logic gates, as ideal current sources. In this study we offer a new methodology for modeling the power-consuming gates on the grid. Our approach is based on the analysis of the total dissipated power by these consumers. We propose a new model for the current consumers, based on effective impedance. In this model, only passive elements are employed. It relies on a calculation of the effective capacitance and effective resistance of the logic gates. Since during each clock period the dissipated power and the stored energy are exactly represented, total energy and power are exactly modeled. Methods from statistical/computational physics can be adopted to represent clusters of consumers on each subgrid as "macro-circuits". The interaction between the power grid and the current consumers is taken into account in this model and an example for it is presented.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9179977" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="34625fd410c8a3d0ddb9141de582503f" rel="nofollow" data-download="{"attachment_id":47860212,"asset_id":9179977,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47860212/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="20975981" href="https://technion.academia.edu/ShaharKvatinsky">Shahar Kvatinsky</a><script data-card-contents-for-user="20975981" type="text/json">{"id":20975981,"first_name":"Shahar","last_name":"Kvatinsky","domain_name":"technion","page_name":"ShaharKvatinsky","display_name":"Shahar Kvatinsky","profile_url":"https://technion.academia.edu/ShaharKvatinsky?f_ri=1351","photo":"https://0.academia-photos.com/20975981/5800308/6591370/s65_shahar.kvatinsky.jpg_oh_5238f56aa54d7a201d9c53df5b50b883_oe_54f64dcc___gda___1423286363_a3c9e605fa704439ec25d8eac573f7a8"}</script></span></span></li><li class="js-paper-rank-work_9179977 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9179977"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9179977, container: ".js-paper-rank-work_9179977", }); 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A typical grid consists of hundreds of millions of transistors that act as current consumers. Typical algorithms for power grid analysis or power grid automatic design, model the current consumers, namely the CMOS logic gates, as ideal current sources. In this study we offer a new methodology for modeling the power-consuming gates on the grid. Our approach is based on the analysis of the total dissipated power by these consumers. We propose a new model for the current consumers, based on effective impedance. In this model, only passive elements are employed. It relies on a calculation of the effective capacitance and effective resistance of the logic gates. Since during each clock period the dissipated power and the stored energy are exactly represented, total energy and power are exactly modeled. Methods from statistical/computational physics can be adopted to represent clusters of consumers on each subgrid as \"macro-circuits\". The interaction between the power grid and the current consumers is taken into account in this model and an example for it is presented.","downloadable_attachments":[{"id":47860212,"asset_id":9179977,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":20975981,"first_name":"Shahar","last_name":"Kvatinsky","domain_name":"technion","page_name":"ShaharKvatinsky","display_name":"Shahar Kvatinsky","profile_url":"https://technion.academia.edu/ShaharKvatinsky?f_ri=1351","photo":"https://0.academia-photos.com/20975981/5800308/6591370/s65_shahar.kvatinsky.jpg_oh_5238f56aa54d7a201d9c53df5b50b883_oe_54f64dcc___gda___1423286363_a3c9e605fa704439ec25d8eac573f7a8"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":210120,"name":"Power Grid","url":"https://www.academia.edu/Documents/in/Power_Grid?f_ri=1351","nofollow":true},{"id":238198,"name":"Seismic analysis and design","url":"https://www.academia.edu/Documents/in/Seismic_analysis_and_design?f_ri=1351","nofollow":true},{"id":322954,"name":"Chip","url":"https://www.academia.edu/Documents/in/Chip?f_ri=1351","nofollow":true},{"id":389570,"name":"Capacitance","url":"https://www.academia.edu/Documents/in/Capacitance?f_ri=1351"},{"id":628286,"name":"Logic Gates","url":"https://www.academia.edu/Documents/in/Logic_Gates?f_ri=1351"},{"id":808173,"name":"Logic Gate","url":"https://www.academia.edu/Documents/in/Logic_Gate?f_ri=1351"},{"id":1247826,"name":"Electric Resistance","url":"https://www.academia.edu/Documents/in/Electric_Resistance?f_ri=1351"},{"id":1935769,"name":"Design Model","url":"https://www.academia.edu/Documents/in/Design_Model?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66535760" data-work_id="66535760" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66535760/General_results_for_the_Kumaraswamy_G_distribution">General results for the Kumaraswamy-G distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article may be used for research, teaching and private study purposes. 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The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.","downloadable_attachments":[{"id":77689108,"asset_id":66535760,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":28215650,"first_name":"Gauss","last_name":"Cordeiro","domain_name":"independent","page_name":"GaussCordeiro","display_name":"Gauss Cordeiro","profile_url":"https://independent.academia.edu/GaussCordeiro?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":17395,"name":"Density-functional theory","url":"https://www.academia.edu/Documents/in/Density-functional_theory?f_ri=1351","nofollow":true},{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=1351"},{"id":80308,"name":"Extreme Value Theory","url":"https://www.academia.edu/Documents/in/Extreme_Value_Theory?f_ri=1351"},{"id":87364,"name":"Maximum Likelihood","url":"https://www.academia.edu/Documents/in/Maximum_Likelihood?f_ri=1351"},{"id":132898,"name":"Estimation","url":"https://www.academia.edu/Documents/in/Estimation?f_ri=1351"},{"id":382710,"name":"System Safety","url":"https://www.academia.edu/Documents/in/System_Safety?f_ri=1351"},{"id":543432,"name":"Exponential distribution","url":"https://www.academia.edu/Documents/in/Exponential_distribution?f_ri=1351"},{"id":570113,"name":"EXTREME VALUES","url":"https://www.academia.edu/Documents/in/EXTREME_VALUES?f_ri=1351"},{"id":700548,"name":"Shannon entropy","url":"https://www.academia.edu/Documents/in/Shannon_entropy?f_ri=1351"},{"id":1969222,"name":"Lorenz Curve","url":"https://www.academia.edu/Documents/in/Lorenz_Curve?f_ri=1351"},{"id":2202802,"name":"Moment generating function method","url":"https://www.academia.edu/Documents/in/Moment_generating_function_method?f_ri=1351"},{"id":2519645,"name":"Order statistic","url":"https://www.academia.edu/Documents/in/Order_statistic?f_ri=1351"},{"id":2560659,"name":"Failure rate","url":"https://www.academia.edu/Documents/in/Failure_rate?f_ri=1351"},{"id":2759436,"name":"Mean Deviation","url":"https://www.academia.edu/Documents/in/Mean_Deviation?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19842445" data-work_id="19842445" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19842445/ISEA_Discrete_Global_Grids">ISEA Discrete Global Grids</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article describes a recently proposed standard, ISEA discrete global grids, for gridding information on the surface of the earth. The acronym ISEA stands for icosahedral Snyder equal area. The grid cells not only have equal areas,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19842445" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article describes a recently proposed standard, ISEA discrete global grids, for gridding information on the surface of the earth. The acronym ISEA stands for icosahedral Snyder equal area. The grid cells not only have equal areas, they are hexagons when projected ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19842445" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="de50ef5f8eae560929e47bdb8b283489" rel="nofollow" data-download="{"attachment_id":42006953,"asset_id":19842445,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42006953/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="40621953" href="https://independent.academia.edu/KevinSahr">Kevin Sahr</a><script data-card-contents-for-user="40621953" type="text/json">{"id":40621953,"first_name":"Kevin","last_name":"Sahr","domain_name":"independent","page_name":"KevinSahr","display_name":"Kevin Sahr","profile_url":"https://independent.academia.edu/KevinSahr?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_19842445 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19842445"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19842445, container: ".js-paper-rank-work_19842445", }); });</script></li><li class="js-percentile-work_19842445 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19842445; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19842445"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19842445 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19842445"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19842445; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19842445]").text(description); $(".js-view-count-work_19842445").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19842445").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19842445"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a><script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19842445]'), work: {"id":19842445,"title":"ISEA Discrete Global Grids","created_at":"2015-12-26T12:00:08.582-08:00","url":"https://www.academia.edu/19842445/ISEA_Discrete_Global_Grids?f_ri=1351","dom_id":"work_19842445","summary":"This article describes a recently proposed standard, ISEA discrete global grids, for gridding information on the surface of the earth. The acronym ISEA stands for icosahedral Snyder equal area. The grid cells not only have equal areas, they are hexagons when projected ...","downloadable_attachments":[{"id":42006953,"asset_id":19842445,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":40621953,"first_name":"Kevin","last_name":"Sahr","domain_name":"independent","page_name":"KevinSahr","display_name":"Kevin Sahr","profile_url":"https://independent.academia.edu/KevinSahr?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20731233" data-work_id="20731233" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20731233/Causal_Mediation_Analysis_Using_R">Causal Mediation Analysis Using R</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal effects.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20731233" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal effects. Recently, Imai, Keele, and Yamamoto (2010c) and developed general algorithms to estimate causal mediation effects with the variety of data types that are often encountered in practice. The new algorithms can estimate causal mediation effects for linear and nonlinear relationships, with parametric and nonparametric models, with continuous and discrete mediators, and various types of outcome variables. In this paper, we show how to implement these algorithms in the statistical computing language R. Our easy-to-use software, mediation, takes advantage of the object-oriented programming nature of the R language and allows researchers to estimate causal mediation effects in a straightforward manner. Finally, mediation also implements sensitivity analyses which can be used to formally assess the robustness of findings to the potential violations of the key identifying assumption. After describing the basic structure of the software, we illustrate its use with several empirical examples. * This paper is an updated version of the tutorial for package mediation which was previously published in an edited volume: Imai et al. (2010a). The description is based on version 3.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20731233" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a23506720382ca0618371ec077196e19" rel="nofollow" data-download="{"attachment_id":41530736,"asset_id":20731233,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41530736/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42027051" href="https://pennstate.academia.edu/LKeele">L. Keele</a><script data-card-contents-for-user="42027051" type="text/json">{"id":42027051,"first_name":"L.","last_name":"Keele","domain_name":"pennstate","page_name":"LKeele","display_name":"L. 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Such an analysis allows researchers to explore various causal pathways, going beyond the estimation of simple causal effects. Recently, Imai, Keele, and Yamamoto (2010c) and developed general algorithms to estimate causal mediation effects with the variety of data types that are often encountered in practice. The new algorithms can estimate causal mediation effects for linear and nonlinear relationships, with parametric and nonparametric models, with continuous and discrete mediators, and various types of outcome variables. In this paper, we show how to implement these algorithms in the statistical computing language R. Our easy-to-use software, mediation, takes advantage of the object-oriented programming nature of the R language and allows researchers to estimate causal mediation effects in a straightforward manner. Finally, mediation also implements sensitivity analyses which can be used to formally assess the robustness of findings to the potential violations of the key identifying assumption. After describing the basic structure of the software, we illustrate its use with several empirical examples. * This paper is an updated version of the tutorial for package mediation which was previously published in an edited volume: Imai et al. (2010a). The description is based on version 3.","downloadable_attachments":[{"id":41530736,"asset_id":20731233,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42027051,"first_name":"L.","last_name":"Keele","domain_name":"pennstate","page_name":"LKeele","display_name":"L. Keele","profile_url":"https://pennstate.academia.edu/LKeele?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":453,"name":"Object Oriented Programming","url":"https://www.academia.edu/Documents/in/Object_Oriented_Programming?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":1197942,"name":"Social Science","url":"https://www.academia.edu/Documents/in/Social_Science?f_ri=1351","nofollow":true},{"id":1547415,"name":"Data Type","url":"https://www.academia.edu/Documents/in/Data_Type?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4886362" data-work_id="4886362" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4886362/Assessing_the_aesthetic_quality_of_photographs_using_generic_image_descriptors">Assessing the aesthetic quality of photographs using generic image descriptors</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4886362" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4886362" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e192bbd878b2bb985a3312a65fb489a5" rel="nofollow" data-download="{"attachment_id":49578926,"asset_id":4886362,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49578926/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6365931" href="https://independent.academia.edu/CsurkaGabriela">Csurka Gabriela</a><script data-card-contents-for-user="6365931" type="text/json">{"id":6365931,"first_name":"Csurka","last_name":"Gabriela","domain_name":"independent","page_name":"CsurkaGabriela","display_name":"Csurka Gabriela","profile_url":"https://independent.academia.edu/CsurkaGabriela?f_ri=1351","photo":"https://0.academia-photos.com/6365931/2590965/3009657/s65_csurka.gabriela.jpg"}</script></span></span></li><li class="js-paper-rank-work_4886362 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4886362"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4886362, container: ".js-paper-rank-work_4886362", }); 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$(".js-view-count[data-work-id=4886362]").text(description); $(".js-view-count-work_4886362").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4886362").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4886362"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1589" rel="nofollow" href="https://www.academia.edu/Documents/in/Photography">Photography</a>, <script data-card-contents-for-ri="1589" type="text/json">{"id":1589,"name":"Photography","url":"https://www.academia.edu/Documents/in/Photography?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5187" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Analysis">Statistical Analysis</a>, <script data-card-contents-for-ri="5187" type="text/json">{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="56368" rel="nofollow" href="https://www.academia.edu/Documents/in/Image_Classification">Image Classification</a><script data-card-contents-for-ri="56368" type="text/json">{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4886362]'), work: {"id":4886362,"title":"Assessing the aesthetic quality of photographs using generic image descriptors","created_at":"2013-10-25T02:51:47.654-07:00","url":"https://www.academia.edu/4886362/Assessing_the_aesthetic_quality_of_photographs_using_generic_image_descriptors?f_ri=1351","dom_id":"work_4886362","summary":"We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.","downloadable_attachments":[{"id":49578926,"asset_id":4886362,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6365931,"first_name":"Csurka","last_name":"Gabriela","domain_name":"independent","page_name":"CsurkaGabriela","display_name":"Csurka Gabriela","profile_url":"https://independent.academia.edu/CsurkaGabriela?f_ri=1351","photo":"https://0.academia-photos.com/6365931/2590965/3009657/s65_csurka.gabriela.jpg"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":1589,"name":"Photography","url":"https://www.academia.edu/Documents/in/Photography?f_ri=1351","nofollow":true},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=1351","nofollow":true},{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=1351","nofollow":true},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=1351"},{"id":571921,"name":"Automatic Assessment","url":"https://www.academia.edu/Documents/in/Automatic_Assessment?f_ri=1351"},{"id":965056,"name":"Local Features","url":"https://www.academia.edu/Documents/in/Local_Features?f_ri=1351"},{"id":1800837,"name":"Image Color Analysis","url":"https://www.academia.edu/Documents/in/Image_Color_Analysis?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5873222" data-work_id="5873222" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5873222/Nonparametric_quality_control_charts_based_on_the_sign_statistic">Nonparametric quality control charts based on the sign statistic</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Nonparametric procedures are presented for the problem of detecting changes in the process median (or mean), or changes in the process variability when samples are taken at regular time intervals. The proposed procedures are based on... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5873222" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Nonparametric procedures are presented for the problem of detecting changes in the process median (or mean), or changes in the process variability when samples are taken at regular time intervals. The proposed procedures are based on sign-test statistics computed for each sample, and are used in Shewhart and cumulative sum control charts. When the process is in control the run length distributions for the proposed nonparametric control charts do not depend on the distribution of the observations. An additional advantage of the non-parametric control charts is that the variance of the process does not need to be established in order to set up a control chart for the mean. Comparisons with the corresponding parametric control charts are presented. It is also shown that curtailed sampling plans can considerably reduce the expected number of observations used in the Shewhart control schemes based on the sign statistic.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5873222" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="969c02d610a5dd1b5f9c35652aec08ca" rel="nofollow" data-download="{"attachment_id":49102369,"asset_id":5873222,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49102369/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1254969" href="https://independent.academia.edu/RaidAmin">Raid Amin</a><script data-card-contents-for-user="1254969" type="text/json">{"id":1254969,"first_name":"Raid","last_name":"Amin","domain_name":"independent","page_name":"RaidAmin","display_name":"Raid Amin","profile_url":"https://independent.academia.edu/RaidAmin?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_5873222 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5873222"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5873222, container: ".js-paper-rank-work_5873222", }); 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$(".js-view-count[data-work-id=5873222]").text(description); $(".js-view-count-work_5873222").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_5873222").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="5873222"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6540" rel="nofollow" href="https://www.academia.edu/Documents/in/Process_Control">Process Control</a>, <script data-card-contents-for-ri="6540" type="text/json">{"id":6540,"name":"Process Control","url":"https://www.academia.edu/Documents/in/Process_Control?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26691" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Process_Control">Statistical Process Control</a><script data-card-contents-for-ri="26691" type="text/json">{"id":26691,"name":"Statistical Process Control","url":"https://www.academia.edu/Documents/in/Statistical_Process_Control?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=5873222]'), work: {"id":5873222,"title":"Nonparametric quality control charts based on the sign statistic","created_at":"2014-01-28T11:52:44.464-08:00","url":"https://www.academia.edu/5873222/Nonparametric_quality_control_charts_based_on_the_sign_statistic?f_ri=1351","dom_id":"work_5873222","summary":"Nonparametric procedures are presented for the problem of detecting changes in the process median (or mean), or changes in the process variability when samples are taken at regular time intervals. The proposed procedures are based on sign-test statistics computed for each sample, and are used in Shewhart and cumulative sum control charts. When the process is in control the run length distributions for the proposed nonparametric control charts do not depend on the distribution of the observations. An additional advantage of the non-parametric control charts is that the variance of the process does not need to be established in order to set up a control chart for the mean. Comparisons with the corresponding parametric control charts are presented. It is also shown that curtailed sampling plans can considerably reduce the expected number of observations used in the Shewhart control schemes based on the sign statistic.","downloadable_attachments":[{"id":49102369,"asset_id":5873222,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1254969,"first_name":"Raid","last_name":"Amin","domain_name":"independent","page_name":"RaidAmin","display_name":"Raid Amin","profile_url":"https://independent.academia.edu/RaidAmin?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":6540,"name":"Process Control","url":"https://www.academia.edu/Documents/in/Process_Control?f_ri=1351","nofollow":true},{"id":26691,"name":"Statistical Process Control","url":"https://www.academia.edu/Documents/in/Statistical_Process_Control?f_ri=1351","nofollow":true},{"id":37306,"name":"Quality Control","url":"https://www.academia.edu/Documents/in/Quality_Control?f_ri=1351"},{"id":483305,"name":"Control chart","url":"https://www.academia.edu/Documents/in/Control_chart?f_ri=1351"},{"id":1993786,"name":"Cumulant","url":"https://www.academia.edu/Documents/in/Cumulant?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14966820" data-work_id="14966820" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14966820/Bayesian_inference_with_optimal_maps">Bayesian inference with optimal maps</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14966820" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selection through automatic evaluation of the marginal likelihood. We demonstrate the accuracy and efficiency of the approach on nonlinear inverse problems of varying dimension, involving the inference of parameters appearing in ordinary and partial differential equations.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14966820" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1301571ab285dfe4c4c256727db29ad3" rel="nofollow" data-download="{"attachment_id":43692711,"asset_id":14966820,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43692711/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33960992" href="https://mit.academia.edu/YoussefMarzouk">Youssef Marzouk</a><script data-card-contents-for-user="33960992" type="text/json">{"id":33960992,"first_name":"Youssef","last_name":"Marzouk","domain_name":"mit","page_name":"YoussefMarzouk","display_name":"Youssef Marzouk","profile_url":"https://mit.academia.edu/YoussefMarzouk?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_14966820 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14966820"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14966820, container: ".js-paper-rank-work_14966820", }); 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Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selection through automatic evaluation of the marginal likelihood. We demonstrate the accuracy and efficiency of the approach on nonlinear inverse problems of varying dimension, involving the inference of parameters appearing in ordinary and partial differential equations.","downloadable_attachments":[{"id":43692711,"asset_id":14966820,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33960992,"first_name":"Youssef","last_name":"Marzouk","domain_name":"mit","page_name":"YoussefMarzouk","display_name":"Youssef Marzouk","profile_url":"https://mit.academia.edu/YoussefMarzouk?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1351","nofollow":true},{"id":504,"name":"Computational Physics","url":"https://www.academia.edu/Documents/in/Computational_Physics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":12022,"name":"Numerical Analysis","url":"https://www.academia.edu/Documents/in/Numerical_Analysis?f_ri=1351","nofollow":true},{"id":46254,"name":"Optimization Problem","url":"https://www.academia.edu/Documents/in/Optimization_Problem?f_ri=1351"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1351"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=1351"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=1351"},{"id":85344,"name":"Model Selection","url":"https://www.academia.edu/Documents/in/Model_Selection?f_ri=1351"},{"id":85345,"name":"Marginal Likelihood","url":"https://www.academia.edu/Documents/in/Marginal_Likelihood?f_ri=1351"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=1351"},{"id":194130,"name":"PARTIAL DIFFERENTIAL EQUATION","url":"https://www.academia.edu/Documents/in/PARTIAL_DIFFERENTIAL_EQUATION?f_ri=1351"},{"id":249561,"name":"Automatic Evaluation","url":"https://www.academia.edu/Documents/in/Automatic_Evaluation?f_ri=1351"},{"id":595993,"name":"Markov chain","url":"https://www.academia.edu/Documents/in/Markov_chain?f_ri=1351"},{"id":1245129,"name":"Nonlinear Inverse Problem","url":"https://www.academia.edu/Documents/in/Nonlinear_Inverse_Problem?f_ri=1351"},{"id":2147708,"name":"Optimal Transportation","url":"https://www.academia.edu/Documents/in/Optimal_Transportation?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9900402" data-work_id="9900402" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9900402/R_package_onlinePCA_Online_Principal_Component_Analysis">R package 'onlinePCA': Online Principal Component Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Online PCA for multivariate and functional data using perturbation, incremental, and stochastic gradient methods.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9900402" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a 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new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=9900402]'), work: {"id":9900402,"title":"R package 'onlinePCA': Online Principal Component Analysis","created_at":"2014-12-25T16:28:40.904-08:00","url":"https://www.academia.edu/9900402/R_package_onlinePCA_Online_Principal_Component_Analysis?f_ri=1351","dom_id":"work_9900402","summary":"Online PCA for multivariate and functional data using perturbation, incremental, and stochastic gradient methods.","downloadable_attachments":[{"id":36057480,"asset_id":9900402,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":150063,"first_name":"David","last_name":"Degras","domain_name":"um-boston","page_name":"DavidDegras","display_name":"David Degras","profile_url":"https://um-boston.academia.edu/DavidDegras?f_ri=1351","photo":"https://0.academia-photos.com/150063/39608/282384/s65_david.degras.jpg"}],"research_interests":[{"id":1351,"name":"Statistical 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href="https://www.academia.edu/45312275/Blockchain_Technology_in_the_Automotive_Industry_Use_Cases_and_Statistical_Evaluation">Blockchain Technology in the Automotive Industry Use Cases and Statistical Evaluation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Even though the automotive industry was among the key players of the industrial revolution in the last century, striking transformations experienced in other sectors did not have significant repercussions on this industry until a few... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45312275" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Even though the automotive industry was among the key players of the industrial revolution in the last century, striking transformations experienced in other sectors did not have significant repercussions on this industry until a few years ago. However, general advancements in technology and Industry 4.0 have presented new opportunities for the reconfiguration of the business environment. Developments in cryptocurrencies such as bitcoin, in particular, have attracted the attention to what is known as block-chain technology. Several successful examples of blockchain applications in different industries have tempted the automotive industry to be rapidly involved with efforts in this direction. As a consequence, the application of the blockchain technology to highly diverse areas in the automotive industry was set in motion. The purpose of this chapter is to explore the application of blockchain technology in the automotive industry, to analyse its advantages and disadvantages, and to demonstrate its successful in general.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45312275" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6979657aae1d534b890780d74e9115e2" rel="nofollow" data-download="{"attachment_id":65859967,"asset_id":45312275,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65859967/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="14503440" href="https://deu.academia.edu/AtakanGerger">Atakan Gerger</a><script data-card-contents-for-user="14503440" type="text/json">{"id":14503440,"first_name":"Atakan","last_name":"Gerger","domain_name":"deu","page_name":"AtakanGerger","display_name":"Atakan Gerger","profile_url":"https://deu.academia.edu/AtakanGerger?f_ri=1351","photo":"https://0.academia-photos.com/14503440/3952765/30020182/s65_atakan.gerger.jpg"}</script></span></span></li><li class="js-paper-rank-work_45312275 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45312275"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45312275, container: ".js-paper-rank-work_45312275", }); 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However, general advancements in technology and Industry 4.0 have presented new opportunities for the reconfiguration of the business environment. Developments in cryptocurrencies such as bitcoin, in particular, have attracted the attention to what is known as block-chain technology. Several successful examples of blockchain applications in different industries have tempted the automotive industry to be rapidly involved with efforts in this direction. As a consequence, the application of the blockchain technology to highly diverse areas in the automotive industry was set in motion. The purpose of this chapter is to explore the application of blockchain technology in the automotive industry, to analyse its advantages and disadvantages, and to demonstrate its successful in general.","downloadable_attachments":[{"id":65859967,"asset_id":45312275,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":14503440,"first_name":"Atakan","last_name":"Gerger","domain_name":"deu","page_name":"AtakanGerger","display_name":"Atakan Gerger","profile_url":"https://deu.academia.edu/AtakanGerger?f_ri=1351","photo":"https://0.academia-photos.com/14503440/3952765/30020182/s65_atakan.gerger.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":258385,"name":"Automotive Technology","url":"https://www.academia.edu/Documents/in/Automotive_Technology?f_ri=1351","nofollow":true},{"id":2199949,"name":"Blockchain","url":"https://www.academia.edu/Documents/in/Blockchain?f_ri=1351","nofollow":true},{"id":2199951,"name":"Blockchain Technologies","url":"https://www.academia.edu/Documents/in/Blockchain_Technologies?f_ri=1351"},{"id":3820724,"name":"Statistical evaluation","url":"https://www.academia.edu/Documents/in/Statistical_evaluation?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15121941 coauthored" data-work_id="15121941" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15121941/Parametric_texture_model_based_on_joint_statistics_of_complex_wavelet_coefficients">Parametric texture model based on joint statistics of complex wavelet coefficients</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15121941" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15121941" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="323107cab15e36e3d197e40ddf1cbe48" rel="nofollow" data-download="{"attachment_id":43565034,"asset_id":15121941,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43565034/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34343989" href="https://nyu.academia.edu/EeroSimoncelli">Eero P Simoncelli</a><script data-card-contents-for-user="34343989" type="text/json">{"id":34343989,"first_name":"Eero","last_name":"Simoncelli","domain_name":"nyu","page_name":"EeroSimoncelli","display_name":"Eero P Simoncelli","profile_url":"https://nyu.academia.edu/EeroSimoncelli?f_ri=1351","photo":"https://0.academia-photos.com/34343989/29269344/27260979/s65_eero.simoncelli.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15121941">+1</span><div class="hidden js-additional-users-15121941"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://csic.academia.edu/JavierPortilla">Javier Portilla</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15121941'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15121941').html(); 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The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.","downloadable_attachments":[{"id":43565034,"asset_id":15121941,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34343989,"first_name":"Eero","last_name":"Simoncelli","domain_name":"nyu","page_name":"EeroSimoncelli","display_name":"Eero P Simoncelli","profile_url":"https://nyu.academia.edu/EeroSimoncelli?f_ri=1351","photo":"https://0.academia-photos.com/34343989/29269344/27260979/s65_eero.simoncelli.jpg"},{"id":34159787,"first_name":"Javier","last_name":"Portilla","domain_name":"csic","page_name":"JavierPortilla","display_name":"Javier Portilla","profile_url":"https://csic.academia.edu/JavierPortilla?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":149059,"name":"Efficient Algorithm for ECG Coding","url":"https://www.academia.edu/Documents/in/Efficient_Algorithm_for_ECG_Coding?f_ri=1351","nofollow":true},{"id":191309,"name":"Texture Synthesis","url":"https://www.academia.edu/Documents/in/Texture_Synthesis?f_ri=1351","nofollow":true},{"id":217116,"name":"Complex wavelet transform","url":"https://www.academia.edu/Documents/in/Complex_wavelet_transform?f_ri=1351"},{"id":664700,"name":"Statistical Model","url":"https://www.academia.edu/Documents/in/Statistical_Model?f_ri=1351"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=1351"},{"id":2287400,"name":"Markov Random Field","url":"https://www.academia.edu/Documents/in/Markov_Random_Field?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11590137" data-work_id="11590137" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11590137/Beta_Regression_in_R">Beta Regression in R</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This introduction to the R package betareg is a (slightly) modified version of Cribari-Neto and Zeileis (2010), published in the Journal of Statistical Software. A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11590137" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This introduction to the R package betareg is a (slightly) modified version of Cribari-Neto and Zeileis (2010), published in the Journal of Statistical Software. A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis (2012) -a slightly modified version of which is also provided within the package as vignette("betareg-ext", package = "betareg")</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11590137" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3312cf701696892ca10529064e44881f" rel="nofollow" data-download="{"attachment_id":46617139,"asset_id":11590137,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46617139/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="28299739" href="https://independent.academia.edu/FranciscoCribarineto">Francisco Cribari-neto</a><script data-card-contents-for-user="28299739" type="text/json">{"id":28299739,"first_name":"Francisco","last_name":"Cribari-neto","domain_name":"independent","page_name":"FranciscoCribarineto","display_name":"Francisco Cribari-neto","profile_url":"https://independent.academia.edu/FranciscoCribarineto?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_11590137 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11590137"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11590137, container: ".js-paper-rank-work_11590137", }); 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A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis (2012) -a slightly modified version of which is also provided within the package as vignette(\"betareg-ext\", package = \"betareg\")","downloadable_attachments":[{"id":46617139,"asset_id":11590137,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":28299739,"first_name":"Francisco","last_name":"Cribari-neto","domain_name":"independent","page_name":"FranciscoCribarineto","display_name":"Francisco Cribari-neto","profile_url":"https://independent.academia.edu/FranciscoCribarineto?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":198462,"name":"Statistical software","url":"https://www.academia.edu/Documents/in/Statistical_software?f_ri=1351","nofollow":true},{"id":406051,"name":"Regression Model","url":"https://www.academia.edu/Documents/in/Regression_Model?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50957248" data-work_id="50957248" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50957248/movecost_R_package_vignette">movecost (R package) vignette</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This vignette aims at showing the use of the current version of the movecost package and of its functions. To hopefully enhance clarity, it is organised as a sequence of tasks. In-built datasets will be used throughout this document. For... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50957248" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This vignette aims at showing the use of the current version of the movecost package and of its functions. To hopefully enhance clarity, it is organised as a sequence of tasks. In-built datasets will be used throughout this document. For more details about each function’s parameter, for the values returned by each function, and for relevant literature, see the package’s help documentation. <br /> <br />For an updated version of the vignette, please visit <a href="https://drive.google.com/file/d/1gLDrkZFh1b_glzCEqKdkPrer72JJ9Ffa/view" rel="nofollow">https://drive.google.com/file/d/1gLDrkZFh1b_glzCEqKdkPrer72JJ9Ffa/view</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50957248" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="19442b3c1d5dda7b830ef484a319b7a2" rel="nofollow" data-download="{"attachment_id":100010281,"asset_id":50957248,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/100010281/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="149398" href="https://malta.academia.edu/GianmarcoAlberti">Gianmarco Alberti</a><script data-card-contents-for-user="149398" type="text/json">{"id":149398,"first_name":"Gianmarco","last_name":"Alberti","domain_name":"malta","page_name":"GianmarcoAlberti","display_name":"Gianmarco Alberti","profile_url":"https://malta.academia.edu/GianmarcoAlberti?f_ri=1351","photo":"https://0.academia-photos.com/149398/39450/2677620/s65_gianmarco.alberti.jpg"}</script></span></span></li><li class="js-paper-rank-work_50957248 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50957248"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50957248, container: ".js-paper-rank-work_50957248", }); 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To hopefully enhance clarity, it is organised as a sequence of tasks. In-built datasets will be used throughout this document. For more details about each function’s parameter, for the values returned by each function, and for relevant literature, see the package’s help documentation.\r\n\r\nFor an updated version of the vignette, please visit https://drive.google.com/file/d/1gLDrkZFh1b_glzCEqKdkPrer72JJ9Ffa/view","downloadable_attachments":[{"id":100010281,"asset_id":50957248,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":149398,"first_name":"Gianmarco","last_name":"Alberti","domain_name":"malta","page_name":"GianmarcoAlberti","display_name":"Gianmarco Alberti","profile_url":"https://malta.academia.edu/GianmarcoAlberti?f_ri=1351","photo":"https://0.academia-photos.com/149398/39450/2677620/s65_gianmarco.alberti.jpg"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":47625,"name":"GIS and Landscape Archaeology","url":"https://www.academia.edu/Documents/in/GIS_and_Landscape_Archaeology?f_ri=1351","nofollow":true},{"id":63954,"name":"Least Cost Path Analysis","url":"https://www.academia.edu/Documents/in/Least_Cost_Path_Analysis?f_ri=1351","nofollow":true},{"id":1009312,"name":"Geographic Information Systems (GIS)","url":"https://www.academia.edu/Documents/in/Geographic_Information_Systems_GIS_?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1743401" data-work_id="1743401" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1743401/R_lista_de_fun%C3%A7%C3%B5es">R - lista de funções</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1743401" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="acbf018cf14111024238e16078c67c3f" rel="nofollow" data-download="{"attachment_id":6115505,"asset_id":1743401,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/6115505/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="831860" href="https://up-pt.academia.edu/JGon%C3%A7alves">João Gonçalves</a><script data-card-contents-for-user="831860" type="text/json">{"id":831860,"first_name":"João","last_name":"Gonçalves","domain_name":"up-pt","page_name":"JGonçalves","display_name":"João Gonçalves","profile_url":"https://up-pt.academia.edu/JGon%C3%A7alves?f_ri=1351","photo":"https://0.academia-photos.com/831860/896824/19797793/s65_jo_o.gon_alves.jpg"}</script></span></span></li><li class="js-paper-rank-work_1743401 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1743401"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1743401, container: ".js-paper-rank-work_1743401", }); 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And only when you answer the question the ``coin'' falls on one of the sides: ``Yes'' or... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37099343" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">You yourself, or what is the same, your experience is such ``coin'' that, while you aren't questioned, it rotates all the time in ``free flight''. 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factors for granted: For the social scientist, software is a tool, not an end in itself. Although there is an extensive literature on statistical computing in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14151835" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A great many empirical researchers in the social sciences take computational factors for granted: For the social scientist, software is a tool, not an end in itself. Although there is an extensive literature on statistical computing in statistics, applied mathematics, and embedded within various natural science fields, there is currently no such guide tailored directly to the needs of the</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14151835" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d9f3f39f5cccfde24c1a4594b73efdcf" rel="nofollow" data-download="{"attachment_id":44560033,"asset_id":14151835,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44560033/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33141428" href="https://independent.academia.edu/AltmanMicah">Micah Altman</a><script data-card-contents-for-user="33141428" type="text/json">{"id":33141428,"first_name":"Micah","last_name":"Altman","domain_name":"independent","page_name":"AltmanMicah","display_name":"Micah Altman","profile_url":"https://independent.academia.edu/AltmanMicah?f_ri=1351","photo":"https://0.academia-photos.com/33141428/10810672/12066017/s65_micah.altman.jpg"}</script></span></span></li><li class="js-paper-rank-work_14151835 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14151835"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14151835, container: ".js-paper-rank-work_14151835", }); 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data-work_id="21355912" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21355912/XLR_A_Free_Excel_Add_In_for_Introductory_Business_Statistics">XLR: A Free Excel Add-In for Introductory Business Statistics</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">XLR is an Excel add-in that unifies the user friendly, widely popular interface of Excel with the powerful and robust computational capability of the GNU statistical and graphical language R. The add-in attempts to address the American... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21355912" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">XLR is an Excel add-in that unifies the user friendly, widely popular interface of Excel with the powerful and robust computational capability of the GNU statistical and graphical language R. The add-in attempts to address the American Statistical Association's comment that "Generic packages such as Excel are not sufficient even for the teaching of statistics, let alone for research and consulting." R is the program of choice for researchers in statistical methodology that is freely available under the Free Software Foundation's GNU General Public License (GPL) Agreement. By wedding the interactive mode of Excel with the power of statistical computing of R, XLR provides a solution to the problem of numerical inaccuracy of using Excel and its various internal statistical functions and procedures by harnessing the computational power of R. XLR will be distributed under the GNU GPL Agreement. The GPL puts students, instructors and researchers in control of their usage of the software by providing them with the freedom to run, copy, distribute, study, change and improve the software, thus, freeing them from the bondage of proprietary software. The creation of XLR will not only have a significant impact on the teaching of an Introductory Business Statistics course by providing a free alternative to the commercial proprietary software but also provide researchers in all disciplines who require sophisticated and cutting edge statistical and graphical procedures with a user-friendly interactive data analysis tool when the current set of available commands is expanded to include more advance procedures.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21355912" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3331222e3f05ec43e6ed90f65716f036" rel="nofollow" data-download="{"attachment_id":41831169,"asset_id":21355912,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41831169/download_file?st=MTc0MDU3MjE4OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5658716" href="https://nau.academia.edu/PinNg">Pin Ng</a><script data-card-contents-for-user="5658716" type="text/json">{"id":5658716,"first_name":"Pin","last_name":"Ng","domain_name":"nau","page_name":"PinNg","display_name":"Pin Ng","profile_url":"https://nau.academia.edu/PinNg?f_ri=1351","photo":"https://0.academia-photos.com/5658716/2460747/2859976/s65_pin.ng.jpg"}</script></span></span></li><li class="js-paper-rank-work_21355912 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21355912"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21355912, container: ".js-paper-rank-work_21355912", }); 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The add-in attempts to address the American Statistical Association's comment that \"Generic packages such as Excel are not sufficient even for the teaching of statistics, let alone for research and consulting.\" R is the program of choice for researchers in statistical methodology that is freely available under the Free Software Foundation's GNU General Public License (GPL) Agreement. By wedding the interactive mode of Excel with the power of statistical computing of R, XLR provides a solution to the problem of numerical inaccuracy of using Excel and its various internal statistical functions and procedures by harnessing the computational power of R. XLR will be distributed under the GNU GPL Agreement. The GPL puts students, instructors and researchers in control of their usage of the software by providing them with the freedom to run, copy, distribute, study, change and improve the software, thus, freeing them from the bondage of proprietary software. 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class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Answer: Based on the presentation on page 665, by "frequency distribution" Pierce means a histogram. And this procedure can be done with the hist() function in R. I created a vector called "digits" and graphed the histogram. The argument... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9805499" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Answer: Based on the presentation on page 665, by "frequency distribution" Pierce means a histogram. And this procedure can be done with the hist() function in R. I created a vector called "digits" and graphed the histogram. The argument main denotes the title of the graph. 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Many regression models are offered. In addition some functions for Bayesian Networks and graphical models are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16348963" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">MXM is an R package which offers variable selection for high-dimensional data in cases of regression and classification. Many regression models are offered. 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class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13794192 coauthored" data-work_id="13794192" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13794192/Accelerated_strength_and_testing_of_concrete_using_blended_cement">Accelerated strength and testing of concrete using blended cement</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Accelerated strength testing using the boiling water procedure of ASTM C 684 was performed to evaluate this test method for use in the routine quality control of concrete made of local materials with particular emphasis on the use of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13794192" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Accelerated strength testing using the boiling water procedure of ASTM C 684 was performed to evaluate this test method for use in the routine quality control of concrete made of local materials with particular emphasis on the use of blended cements, and in the prediction of potential quality and strength of concrete at later ages. Large number of groups of standard concrete specimens are sampled;</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13794192" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7530e2a1299f1034085b267954071ee2" rel="nofollow" data-download="{"attachment_id":44941560,"asset_id":13794192,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44941560/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32897224" href="https://independent.academia.edu/MResheidat">Musa Resheidat</a><script data-card-contents-for-user="32897224" type="text/json">{"id":32897224,"first_name":"Musa","last_name":"Resheidat","domain_name":"independent","page_name":"MResheidat","display_name":"Musa Resheidat","profile_url":"https://independent.academia.edu/MResheidat?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-13794192">+1</span><div class="hidden js-additional-users-13794192"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MwafagGhanma">Mwafag Ghanma</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-13794192'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-13794192').html(); 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Large number of groups of standard concrete specimens are sampled;","downloadable_attachments":[{"id":44941560,"asset_id":13794192,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32897224,"first_name":"Musa","last_name":"Resheidat","domain_name":"independent","page_name":"MResheidat","display_name":"Musa Resheidat","profile_url":"https://independent.academia.edu/MResheidat?f_ri=1351","photo":"/images/s65_no_pic.png"},{"id":6392043,"first_name":"Mwafag","last_name":"Ghanma","domain_name":"independent","page_name":"MwafagGhanma","display_name":"Mwafag Ghanma","profile_url":"https://independent.academia.edu/MwafagGhanma?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":37306,"name":"Quality Control","url":"https://www.academia.edu/Documents/in/Quality_Control?f_ri=1351","nofollow":true},{"id":97641,"name":"Compressive Strength","url":"https://www.academia.edu/Documents/in/Compressive_Strength?f_ri=1351","nofollow":true},{"id":123230,"name":"Regression Analysis","url":"https://www.academia.edu/Documents/in/Regression_Analysis?f_ri=1351","nofollow":true},{"id":224767,"name":"Prediction Model","url":"https://www.academia.edu/Documents/in/Prediction_Model?f_ri=1351"},{"id":472309,"name":"Advanced cement-based materials","url":"https://www.academia.edu/Documents/in/Advanced_cement-based_materials?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1513798" data-work_id="1513798" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1513798/Survey_of_clustering_algorithms">Survey of clustering algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1513798" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1513798" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bf0352c865c5283e008badda88ba2435" rel="nofollow" data-download="{"attachment_id":12173759,"asset_id":1513798,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/12173759/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1534432" href="https://atbunet.academia.edu/IbitoyeAdeniyiFrancis">Ibitoye Adeniyi Francis</a><script data-card-contents-for-user="1534432" type="text/json">{"id":1534432,"first_name":"Ibitoye","last_name":"Adeniyi Francis","domain_name":"atbunet","page_name":"IbitoyeAdeniyiFrancis","display_name":"Ibitoye Adeniyi Francis","profile_url":"https://atbunet.academia.edu/IbitoyeAdeniyiFrancis?f_ri=1351","photo":"https://0.academia-photos.com/1534432/542740/678017/s65_ibitoye.adeniyi_francis.jpg"}</script></span></span></li><li class="js-paper-rank-work_1513798 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1513798"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1513798, container: ".js-paper-rank-work_1513798", }); 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Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.","downloadable_attachments":[{"id":12173759,"asset_id":1513798,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1534432,"first_name":"Ibitoye","last_name":"Adeniyi Francis","domain_name":"atbunet","page_name":"IbitoyeAdeniyiFrancis","display_name":"Ibitoye Adeniyi Francis","profile_url":"https://atbunet.academia.edu/IbitoyeAdeniyiFrancis?f_ri=1351","photo":"https://0.academia-photos.com/1534432/542740/678017/s65_ibitoye.adeniyi_francis.jpg"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=1351","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1351"},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=1351"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=1351"},{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=1351"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=1351"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=1351"},{"id":43131,"name":"Stochastic processes","url":"https://www.academia.edu/Documents/in/Stochastic_processes?f_ri=1351"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=1351"},{"id":84990,"name":"Clustering","url":"https://www.academia.edu/Documents/in/Clustering?f_ri=1351"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis?f_ri=1351"},{"id":160328,"name":"Prior Knowledge","url":"https://www.academia.edu/Documents/in/Prior_Knowledge?f_ri=1351"},{"id":202672,"name":"Adaptive Resonance Theory","url":"https://www.academia.edu/Documents/in/Adaptive_Resonance_Theory?f_ri=1351"},{"id":217153,"name":"Traveling Salesman Problem","url":"https://www.academia.edu/Documents/in/Traveling_Salesman_Problem?f_ri=1351"},{"id":254626,"name":"Cluster","url":"https://www.academia.edu/Documents/in/Cluster?f_ri=1351"},{"id":533928,"name":"Proximity","url":"https://www.academia.edu/Documents/in/Proximity?f_ri=1351"},{"id":852989,"name":"Clustering Algorithm","url":"https://www.academia.edu/Documents/in/Clustering_Algorithm?f_ri=1351"},{"id":1931321,"name":"Application Software","url":"https://www.academia.edu/Documents/in/Application_Software?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15702317 coauthored" data-work_id="15702317" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15702317/Calibration_and_Filtering_for_Multi_Factor_Commodity_Models_with_Seasonality_Incorporating_Panel_Data_from_Futures_Contracts">Calibration and Filtering for Multi Factor Commodity Models with Seasonality: Incorporating Panel Data from Futures Contracts</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both the long and short term dynamic... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15702317" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stochastic volatility factors and secondly we develop an additive structural seasonality model. Then a Milstein discretized non-linear stochastic volatility state space representation for the model is developed which allows for futures and options contracts in the observation equation. We then develop numerical methodology based on an advanced Sequential Monte Carlo algorithm utilising Particle Markov chain Monte Carlo to perform calibration of the model jointly with the filtering of the latent processes for the long-short dynamics and volatility factors. In this regard we explore and develop a novel methodology based on an adaptive Rao-Blackwellised version of the Particle Markov chain Monte Carlo methodology. In doing this we deal accurately with the non-linearities in the state-space model which are therefore introduced into the filtering framework. We perform analysis on synthetic and real data for oil commodities.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15702317" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="757f5cf322d9dadae22be81838669569" rel="nofollow" data-download="{"attachment_id":42954669,"asset_id":15702317,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42954669/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34891141" href="https://ucsb.academia.edu/GarethPeters">Gareth Peters</a><script data-card-contents-for-user="34891141" type="text/json">{"id":34891141,"first_name":"Gareth","last_name":"Peters","domain_name":"ucsb","page_name":"GarethPeters","display_name":"Gareth Peters","profile_url":"https://ucsb.academia.edu/GarethPeters?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15702317">+1</span><div class="hidden js-additional-users-15702317"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/PavelShevchenko">Pavel Shevchenko</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15702317'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15702317').html(); 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We extend the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stochastic volatility factors and secondly we develop an additive structural seasonality model. Then a Milstein discretized non-linear stochastic volatility state space representation for the model is developed which allows for futures and options contracts in the observation equation. We then develop numerical methodology based on an advanced Sequential Monte Carlo algorithm utilising Particle Markov chain Monte Carlo to perform calibration of the model jointly with the filtering of the latent processes for the long-short dynamics and volatility factors. In this regard we explore and develop a novel methodology based on an adaptive Rao-Blackwellised version of the Particle Markov chain Monte Carlo methodology. In doing this we deal accurately with the non-linearities in the state-space model which are therefore introduced into the filtering framework. We perform analysis on synthetic and real data for oil commodities.","downloadable_attachments":[{"id":42954669,"asset_id":15702317,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34891141,"first_name":"Gareth","last_name":"Peters","domain_name":"ucsb","page_name":"GarethPeters","display_name":"Gareth Peters","profile_url":"https://ucsb.academia.edu/GarethPeters?f_ri=1351","photo":"/images/s65_no_pic.png"},{"id":35099821,"first_name":"Pavel","last_name":"Shevchenko","domain_name":"independent","page_name":"PavelShevchenko","display_name":"Pavel Shevchenko","profile_url":"https://independent.academia.edu/PavelShevchenko?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":3527,"name":"Computational Finance","url":"https://www.academia.edu/Documents/in/Computational_Finance?f_ri=1351","nofollow":true},{"id":10552,"name":"Sequential Monte Carlo","url":"https://www.academia.edu/Documents/in/Sequential_Monte_Carlo?f_ri=1351"},{"id":40860,"name":"Panel Data","url":"https://www.academia.edu/Documents/in/Panel_Data?f_ri=1351"},{"id":51212,"name":"Performance Analysis","url":"https://www.academia.edu/Documents/in/Performance_Analysis?f_ri=1351"},{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality?f_ri=1351"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=1351"},{"id":87533,"name":"Stochastic Volatility","url":"https://www.academia.edu/Documents/in/Stochastic_Volatility?f_ri=1351"},{"id":514741,"name":"Five Factor Model","url":"https://www.academia.edu/Documents/in/Five_Factor_Model?f_ri=1351"},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11946435" data-work_id="11946435" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11946435/GENERATING_SIMPLIFIED_STATISTICAL_POPULATION_PROFILES_TO_INFORM_LOCAL_GOVERNMENT_POLICIES_AND_PLANNING">GENERATING SIMPLIFIED STATISTICAL POPULATION PROFILES TO INFORM LOCAL GOVERNMENT POLICIES AND PLANNING</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The paper reports on both methodological and substantive findings. It presents a method for generating simplified representations for regional urban populations, their geographical sub-populations and communities. the method generates... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11946435" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The paper reports on both methodological and substantive findings. It presents a method for generating simplified representations for regional urban populations, their geographical sub-populations and communities. the method generates greatly simplified high-resolution socio-economic profiles of populated geographical areas from complex large census data sets.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11946435" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6516d7b27ec904f6dab89df533e09f69" rel="nofollow" data-download="{"attachment_id":37306419,"asset_id":11946435,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37306419/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="18878088" href="https://independent.academia.edu/trevorwren">Trevor Wren BSc MA MPhil PhD MBA</a><script data-card-contents-for-user="18878088" type="text/json">{"id":18878088,"first_name":"Trevor","last_name":"Wren BSc MA MPhil PhD MBA","domain_name":"independent","page_name":"trevorwren","display_name":"Trevor Wren BSc MA MPhil PhD MBA","profile_url":"https://independent.academia.edu/trevorwren?f_ri=1351","photo":"https://0.academia-photos.com/18878088/5247911/6001811/s65_trevor.wren.jpg_oh_12adb13cb0c446279d6b0139cd929fbc_oe_54f03f51___gda___1425507246_85468fc3920de1b4fce8638dfb1ba988"}</script></span></span></li><li class="js-paper-rank-work_11946435 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11946435"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11946435, container: ".js-paper-rank-work_11946435", }); 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href="https://www.academia.edu/8331727/A_general_framework_for_parametric_survival_analysis">A general framework for parametric survival analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8331727" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8331727" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="67bbb1a65023f5cc23b12708633d3f69" rel="nofollow" data-download="{"attachment_id":38798797,"asset_id":8331727,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38798797/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="643825" href="https://leicester.academia.edu/MichaelCrowther">Michael Crowther</a><script data-card-contents-for-user="643825" type="text/json">{"id":643825,"first_name":"Michael","last_name":"Crowther","domain_name":"leicester","page_name":"MichaelCrowther","display_name":"Michael Crowther","profile_url":"https://leicester.academia.edu/MichaelCrowther?f_ri=1351","photo":"https://0.academia-photos.com/643825/679823/10890579/s65_michael.crowther.jpg"}</script></span></span></li><li class="js-paper-rank-work_8331727 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8331727"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8331727, container: ".js-paper-rank-work_8331727", }); 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$(".js-view-count[data-work-id=8331727]").text(description); $(".js-view-count-work_8331727").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8331727").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8331727"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3058" rel="nofollow" href="https://www.academia.edu/Documents/in/Biostatistics">Biostatistics</a>, <script data-card-contents-for-ri="3058" type="text/json">{"id":3058,"name":"Biostatistics","url":"https://www.academia.edu/Documents/in/Biostatistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10610" rel="nofollow" href="https://www.academia.edu/Documents/in/Survival_Analysis">Survival Analysis</a><script data-card-contents-for-ri="10610" type="text/json">{"id":10610,"name":"Survival Analysis","url":"https://www.academia.edu/Documents/in/Survival_Analysis?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8331727]'), work: {"id":8331727,"title":"A general framework for parametric survival analysis","created_at":"2014-09-15T01:04:27.390-07:00","url":"https://www.academia.edu/8331727/A_general_framework_for_parametric_survival_analysis?f_ri=1351","dom_id":"work_8331727","summary":"Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley \u0026 Sons, Ltd.","downloadable_attachments":[{"id":38798797,"asset_id":8331727,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":643825,"first_name":"Michael","last_name":"Crowther","domain_name":"leicester","page_name":"MichaelCrowther","display_name":"Michael Crowther","profile_url":"https://leicester.academia.edu/MichaelCrowther?f_ri=1351","photo":"https://0.academia-photos.com/643825/679823/10890579/s65_michael.crowther.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":3058,"name":"Biostatistics","url":"https://www.academia.edu/Documents/in/Biostatistics?f_ri=1351","nofollow":true},{"id":10610,"name":"Survival Analysis","url":"https://www.academia.edu/Documents/in/Survival_Analysis?f_ri=1351","nofollow":true},{"id":63359,"name":"Stata","url":"https://www.academia.edu/Documents/in/Stata?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12797474 coauthored" data-work_id="12797474" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12797474/An_introduction_to_recursive_partitioning_Rationale_application_and_characteristics_of_classification_and_regression_trees_bagging_and_random_forests">An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12797474" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12797474" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c4fd39e28445d7e604d265e44cea7cdc" rel="nofollow" data-download="{"attachment_id":45928625,"asset_id":12797474,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45928625/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31870355" href="https://independent.academia.edu/GerhardTutz">Gerhard Tutz</a><script data-card-contents-for-user="31870355" type="text/json">{"id":31870355,"first_name":"Gerhard","last_name":"Tutz","domain_name":"independent","page_name":"GerhardTutz","display_name":"Gerhard Tutz","profile_url":"https://independent.academia.edu/GerhardTutz?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-12797474">+1</span><div class="hidden js-additional-users-12797474"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/JamesMalley1">James Malley</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-12797474'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-12797474').html(); 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Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.","downloadable_attachments":[{"id":45928625,"asset_id":12797474,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31870355,"first_name":"Gerhard","last_name":"Tutz","domain_name":"independent","page_name":"GerhardTutz","display_name":"Gerhard Tutz","profile_url":"https://independent.academia.edu/GerhardTutz?f_ri=1351","photo":"/images/s65_no_pic.png"},{"id":41133904,"first_name":"James","last_name":"Malley","domain_name":"independent","page_name":"JamesMalley1","display_name":"James Malley","profile_url":"https://independent.academia.edu/JamesMalley1?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=1351","nofollow":true},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=1351","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=1351","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351"},{"id":2065,"name":"Research Methodology","url":"https://www.academia.edu/Documents/in/Research_Methodology?f_ri=1351"},{"id":3243,"name":"Nonparametric Statistics","url":"https://www.academia.edu/Documents/in/Nonparametric_Statistics?f_ri=1351"},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=1351"},{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction?f_ri=1351"},{"id":41482,"name":"Multivariate Analysis","url":"https://www.academia.edu/Documents/in/Multivariate_Analysis?f_ri=1351"},{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest?f_ri=1351"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=1351"},{"id":107672,"name":"Regression","url":"https://www.academia.edu/Documents/in/Regression?f_ri=1351"},{"id":123230,"name":"Regression Analysis","url":"https://www.academia.edu/Documents/in/Regression_Analysis?f_ri=1351"},{"id":161594,"name":"High Dimensional Data","url":"https://www.academia.edu/Documents/in/High_Dimensional_Data?f_ri=1351"},{"id":180204,"name":"Nonparametric Regression","url":"https://www.academia.edu/Documents/in/Nonparametric_Regression?f_ri=1351"},{"id":263097,"name":"Application","url":"https://www.academia.edu/Documents/in/Application?f_ri=1351"},{"id":339534,"name":"Classification and Regression Tree","url":"https://www.academia.edu/Documents/in/Classification_and_Regression_Tree?f_ri=1351"},{"id":493224,"name":"Recursivity","url":"https://www.academia.edu/Documents/in/Recursivity?f_ri=1351"},{"id":557801,"name":"High Dimensionality","url":"https://www.academia.edu/Documents/in/High_Dimensionality?f_ri=1351"},{"id":816819,"name":"Psychological Models","url":"https://www.academia.edu/Documents/in/Psychological_Models?f_ri=1351"},{"id":1092441,"name":"Psychological Methods","url":"https://www.academia.edu/Documents/in/Psychological_Methods?f_ri=1351"},{"id":1293408,"name":"Tree Graph","url":"https://www.academia.edu/Documents/in/Tree_Graph?f_ri=1351"},{"id":1553926,"name":"Statistical Regression","url":"https://www.academia.edu/Documents/in/Statistical_Regression?f_ri=1351"},{"id":1671808,"name":"Prediction Accuracy","url":"https://www.academia.edu/Documents/in/Prediction_Accuracy?f_ri=1351"},{"id":2435000,"name":"Subdivision","url":"https://www.academia.edu/Documents/in/Subdivision?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5575983" data-work_id="5575983" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5575983/MODIFIED_MEDIAN_TEST_INTRINSICALLY_ADJUSTED_FOR_TIES">MODIFIED MEDIAN TEST INTRINSICALLY ADJUSTED FOR TIES</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper proposes a modified median test that intrinsically adjusts for the possible presence of ties in observations in two sample data. The propose method is illustrated with some data and the test statistics is shown to perform at... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5575983" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper proposes a modified median test that intrinsically adjusts for the possible presence of ties in observations in two sample data. The propose method is illustrated with some data and the test statistics is shown to perform at least in terms of power and efficiency. The method also enables the easy isolation of any ties in the data and the estimation of their probabilities of occurrence</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5575983" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b49cb25c67e0c3535518a813222a5feb" rel="nofollow" data-download="{"attachment_id":32662868,"asset_id":5575983,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/32662868/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7941589" href="https://manchester.academia.edu/EmmanuelAfuecheta">Emmanuel Afuecheta</a><script data-card-contents-for-user="7941589" type="text/json">{"id":7941589,"first_name":"Emmanuel","last_name":"Afuecheta","domain_name":"manchester","page_name":"EmmanuelAfuecheta","display_name":"Emmanuel Afuecheta","profile_url":"https://manchester.academia.edu/EmmanuelAfuecheta?f_ri=1351","photo":"https://0.academia-photos.com/7941589/2790292/3253148/s65_emmanuel.afuecheta.jpg"}</script></span></span></li><li class="js-paper-rank-work_5575983 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5575983"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5575983, container: ".js-paper-rank-work_5575983", }); 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The propose method is illustrated with some data and the test statistics is shown to perform at least in terms of power and efficiency. The method also enables the easy isolation of any ties in the data and the estimation of their probabilities of occurrence","downloadable_attachments":[{"id":32662868,"asset_id":5575983,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7941589,"first_name":"Emmanuel","last_name":"Afuecheta","domain_name":"manchester","page_name":"EmmanuelAfuecheta","display_name":"Emmanuel Afuecheta","profile_url":"https://manchester.academia.edu/EmmanuelAfuecheta?f_ri=1351","photo":"https://0.academia-photos.com/7941589/2790292/3253148/s65_emmanuel.afuecheta.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=1351","nofollow":true},{"id":9961,"name":"Time series Econometrics","url":"https://www.academia.edu/Documents/in/Time_series_Econometrics?f_ri=1351","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=1351"},{"id":80308,"name":"Extreme Value Theory","url":"https://www.academia.edu/Documents/in/Extreme_Value_Theory?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1224736" data-work_id="1224736" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1224736/Engineering_optimisation_by_cuckoo_search">Engineering optimisation by cuckoo search</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by . This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1224736" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by . This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1224736" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b0afe6f6e438facd03937f6a4b738720" rel="nofollow" data-download="{"attachment_id":7629802,"asset_id":1224736,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/7629802/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1129740" href="https://annauniv.academia.edu/balamonica">bala monica</a><script data-card-contents-for-user="1129740" type="text/json">{"id":1129740,"first_name":"bala","last_name":"monica","domain_name":"annauniv","page_name":"balamonica","display_name":"bala monica","profile_url":"https://annauniv.academia.edu/balamonica?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_1224736 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1224736"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1224736, container: ".js-paper-rank-work_1224736", }); 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$(".js-view-count[data-work-id=1224736]").text(description); $(".js-view-count-work_1224736").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1224736").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1224736"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7947" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering_Design">Engineering Design</a>, <script data-card-contents-for-ri="7947" type="text/json">{"id":7947,"name":"Engineering Design","url":"https://www.academia.edu/Documents/in/Engineering_Design?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="16682" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Modelling">Mathematical Modelling</a>, <script data-card-contents-for-ri="16682" type="text/json">{"id":16682,"name":"Mathematical Modelling","url":"https://www.academia.edu/Documents/in/Mathematical_Modelling?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29417" rel="nofollow" href="https://www.academia.edu/Documents/in/Particle_Swarm_Optimisation">Particle Swarm Optimisation</a><script data-card-contents-for-ri="29417" type="text/json">{"id":29417,"name":"Particle Swarm Optimisation","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimisation?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1224736]'), work: {"id":1224736,"title":"Engineering optimisation by cuckoo search","created_at":"2012-01-16T23:03:32.107-08:00","url":"https://www.academia.edu/1224736/Engineering_optimisation_by_cuckoo_search?f_ri=1351","dom_id":"work_1224736","summary":"A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by . This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.","downloadable_attachments":[{"id":7629802,"asset_id":1224736,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1129740,"first_name":"bala","last_name":"monica","domain_name":"annauniv","page_name":"balamonica","display_name":"bala monica","profile_url":"https://annauniv.academia.edu/balamonica?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":7947,"name":"Engineering Design","url":"https://www.academia.edu/Documents/in/Engineering_Design?f_ri=1351","nofollow":true},{"id":16682,"name":"Mathematical Modelling","url":"https://www.academia.edu/Documents/in/Mathematical_Modelling?f_ri=1351","nofollow":true},{"id":29417,"name":"Particle Swarm Optimisation","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimisation?f_ri=1351","nofollow":true},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics?f_ri=1351"},{"id":1993320,"name":"Optimal Solution","url":"https://www.academia.edu/Documents/in/Optimal_Solution?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12652354" data-work_id="12652354" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12652354/snp_plotter_an_R_based_SNP_haplotype_association_and_linkage_disequilibrium_plotting_package">snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">snp.plotter is a newly developed R package which produces high-quality plots of results from genetic association studies. The main features of the package include options to display a linkage disequilibrium (LD) plot below the P-value... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12652354" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">snp.plotter is a newly developed R package which produces high-quality plots of results from genetic association studies. The main features of the package include options to display a linkage disequilibrium (LD) plot below the P-value plot using either the r 2 or D 0 LD metric, to set the X-axis to equal spacing or to use the physical map of markers, and to specify plot labels, colors, symbols and LD heatmap color scheme. snp.plotter can plot single SNP and/ or haplotype data and simultaneously plot multiple sets of results. R is a free software environment for statistical computing and graphics available for most platforms. The proposed package provides a simple way to convey both association and LD information in a single appealing graphic for genetic association studies. Availability: Downloadable R package and example datasets are available at <a href="http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html" rel="nofollow">http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html</a> and <a href="http://www.r-project.org" rel="nofollow">http://www.r-project.org</a> Contact: <a href="mailto:nicodemusk@mail.nih.gov" rel="nofollow">nicodemusk@mail.nih.gov</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12652354" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4fca21883a1882510c5c36c5f8ed0654" rel="nofollow" data-download="{"attachment_id":46026749,"asset_id":12652354,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46026749/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31637977" href="https://independent.academia.edu/KristinNicodemus">Kristin Nicodemus</a><script data-card-contents-for-user="31637977" type="text/json">{"id":31637977,"first_name":"Kristin","last_name":"Nicodemus","domain_name":"independent","page_name":"KristinNicodemus","display_name":"Kristin Nicodemus","profile_url":"https://independent.academia.edu/KristinNicodemus?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_12652354 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12652354"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12652354, container: ".js-paper-rank-work_12652354", }); 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$(".js-view-count[data-work-id=12652354]").text(description); $(".js-view-count-work_12652354").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12652354").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12652354"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="146" rel="nofollow" href="https://www.academia.edu/Documents/in/Bioinformatics">Bioinformatics</a>, <script data-card-contents-for-ri="146" type="text/json">{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="445" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Graphics">Computer Graphics</a>, <script data-card-contents-for-ri="445" type="text/json">{"id":445,"name":"Computer Graphics","url":"https://www.academia.edu/Documents/in/Computer_Graphics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="451" rel="nofollow" href="https://www.academia.edu/Documents/in/Programming_Languages">Programming Languages</a><script data-card-contents-for-ri="451" type="text/json">{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12652354]'), work: {"id":12652354,"title":"snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package","created_at":"2015-05-28T08:29:22.152-07:00","url":"https://www.academia.edu/12652354/snp_plotter_an_R_based_SNP_haplotype_association_and_linkage_disequilibrium_plotting_package?f_ri=1351","dom_id":"work_12652354","summary":"snp.plotter is a newly developed R package which produces high-quality plots of results from genetic association studies. The main features of the package include options to display a linkage disequilibrium (LD) plot below the P-value plot using either the r 2 or D 0 LD metric, to set the X-axis to equal spacing or to use the physical map of markers, and to specify plot labels, colors, symbols and LD heatmap color scheme. snp.plotter can plot single SNP and/ or haplotype data and simultaneously plot multiple sets of results. R is a free software environment for statistical computing and graphics available for most platforms. The proposed package provides a simple way to convey both association and LD information in a single appealing graphic for genetic association studies. Availability: Downloadable R package and example datasets are available at http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html and http://www.r-project.org Contact: nicodemusk@mail.nih.gov","downloadable_attachments":[{"id":46026749,"asset_id":12652354,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31637977,"first_name":"Kristin","last_name":"Nicodemus","domain_name":"independent","page_name":"KristinNicodemus","display_name":"Kristin Nicodemus","profile_url":"https://independent.academia.edu/KristinNicodemus?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=1351","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1351","nofollow":true},{"id":445,"name":"Computer Graphics","url":"https://www.academia.edu/Documents/in/Computer_Graphics?f_ri=1351","nofollow":true},{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351"},{"id":31408,"name":"Free Software","url":"https://www.academia.edu/Documents/in/Free_Software?f_ri=1351"},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences?f_ri=1351"},{"id":53293,"name":"Software","url":"https://www.academia.edu/Documents/in/Software?f_ri=1351"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=1351"},{"id":86952,"name":"Haplotypes","url":"https://www.academia.edu/Documents/in/Haplotypes?f_ri=1351"},{"id":255094,"name":"Computer User Interface Design","url":"https://www.academia.edu/Documents/in/Computer_User_Interface_Design?f_ri=1351"},{"id":486793,"name":"Physical Map","url":"https://www.academia.edu/Documents/in/Physical_Map?f_ri=1351"},{"id":489736,"name":"Linkage Disequilibrium","url":"https://www.academia.edu/Documents/in/Linkage_Disequilibrium?f_ri=1351"},{"id":735504,"name":"Genetic Association","url":"https://www.academia.edu/Documents/in/Genetic_Association?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23197269" data-work_id="23197269" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23197269/A_Study_of_Efficient_Estimation_Methods_for_the_parameters_of_Extreme_Value_Distribution_by_Utilizing_Monte_Carlo_Sampling">A Study of Efficient Estimation Methods for the parameters of Extreme Value Distribution by Utilizing Monte Carlo Sampling</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this thesis, we consider the extreme value distn. of two parameters for the reason of its appearance in many statistical fields of applications. Mathematical and statistical properties of the distribution. such as moments and higher... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23197269" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this thesis, we consider the extreme value distn. of two parameters for the reason of its appearance in many statistical fields of applications. Mathematical and statistical properties of the distribution. such as moments and higher moments are collected and unified and the properties of reliability and hazard functions of the distribution are illustrated.<br />The chi-square goodness - of - fit is used to test whether the generated samples from the standardized extreme value distribution by Monte Carlo simulation are acceptable for use.<br />These samples are used to estimate the distribution parameters by four methods of estimation, namely moments method, maximum likelihood method, order statistic method and least squares method.<br />These methods are discussed theoretically and assessed practically in estimating the reliability and hazard functions. The properties of the estimator, reliability and hazard functions, such as bias, variance, skewness, kurtosis, and mean square error are tabled.<br />The computer programs are listed in three appendices and the run is made by using "MathCAD 14".</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23197269" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0a644cda12923d92be24c18c7ada63a0" rel="nofollow" data-download="{"attachment_id":43686363,"asset_id":23197269,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43686363/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8050147" href="https://independent.academia.edu/FadiShabo">Fadi Shaayo</a><script data-card-contents-for-user="8050147" type="text/json">{"id":8050147,"first_name":"Fadi","last_name":"Shaayo","domain_name":"independent","page_name":"FadiShabo","display_name":"Fadi Shaayo","profile_url":"https://independent.academia.edu/FadiShabo?f_ri=1351","photo":"https://0.academia-photos.com/8050147/11949082/13314669/s65_fadi.shabo.jpg"}</script></span></span></li><li class="js-paper-rank-work_23197269 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23197269"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23197269, container: ".js-paper-rank-work_23197269", }); 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$(".js-view-count[data-work-id=23197269]").text(description); $(".js-view-count-work_23197269").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23197269").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23197269"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="520" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Mechanics">Statistical Mechanics</a>, <script data-card-contents-for-ri="520" type="text/json">{"id":520,"name":"Statistical Mechanics","url":"https://www.academia.edu/Documents/in/Statistical_Mechanics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1352" rel="nofollow" href="https://www.academia.edu/Documents/in/Multivariate_Statistics">Multivariate Statistics</a><script data-card-contents-for-ri="1352" type="text/json">{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23197269]'), work: {"id":23197269,"title":"A Study of Efficient Estimation Methods for the parameters of Extreme Value Distribution by Utilizing Monte Carlo Sampling","created_at":"2016-03-13T09:21:28.988-07:00","url":"https://www.academia.edu/23197269/A_Study_of_Efficient_Estimation_Methods_for_the_parameters_of_Extreme_Value_Distribution_by_Utilizing_Monte_Carlo_Sampling?f_ri=1351","dom_id":"work_23197269","summary":"In this thesis, we consider the extreme value distn. of two parameters for the reason of its appearance in many statistical fields of applications. Mathematical and statistical properties of the distribution. such as moments and higher moments are collected and unified and the properties of reliability and hazard functions of the distribution are illustrated.\nThe chi-square goodness - of - fit is used to test whether the generated samples from the standardized extreme value distribution by Monte Carlo simulation are acceptable for use.\nThese samples are used to estimate the distribution parameters by four methods of estimation, namely moments method, maximum likelihood method, order statistic method and least squares method.\nThese methods are discussed theoretically and assessed practically in estimating the reliability and hazard functions. The properties of the estimator, reliability and hazard functions, such as bias, variance, skewness, kurtosis, and mean square error are tabled.\nThe computer programs are listed in three appendices and the run is made by using \"MathCAD 14\".","downloadable_attachments":[{"id":43686363,"asset_id":23197269,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":8050147,"first_name":"Fadi","last_name":"Shaayo","domain_name":"independent","page_name":"FadiShabo","display_name":"Fadi Shaayo","profile_url":"https://independent.academia.edu/FadiShabo?f_ri=1351","photo":"https://0.academia-photos.com/8050147/11949082/13314669/s65_fadi.shabo.jpg"}],"research_interests":[{"id":520,"name":"Statistical Mechanics","url":"https://www.academia.edu/Documents/in/Statistical_Mechanics?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=1351","nofollow":true},{"id":2606,"name":"Innovation statistics","url":"https://www.academia.edu/Documents/in/Innovation_statistics?f_ri=1351"},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=1351"},{"id":4388,"name":"Computational Statistics","url":"https://www.academia.edu/Documents/in/Computational_Statistics?f_ri=1351"},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=1351"},{"id":14585,"name":"Statistical Modeling","url":"https://www.academia.edu/Documents/in/Statistical_Modeling?f_ri=1351"},{"id":15084,"name":"Statistical machine learning","url":"https://www.academia.edu/Documents/in/Statistical_machine_learning?f_ri=1351"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=1351"},{"id":41239,"name":"Bayesian statistics \u0026 modelling","url":"https://www.academia.edu/Documents/in/Bayesian_statistics_and_modelling?f_ri=1351"},{"id":83635,"name":"OPerations research and statistics","url":"https://www.academia.edu/Documents/in/OPerations_research_and_statistics?f_ri=1351"},{"id":98937,"name":"Statistic","url":"https://www.academia.edu/Documents/in/Statistic?f_ri=1351"},{"id":265402,"name":"Applied Mathematics and Statistics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics_and_Statistics?f_ri=1351"},{"id":388873,"name":"Mathematics and Statistics","url":"https://www.academia.edu/Documents/in/Mathematics_and_Statistics?f_ri=1351"},{"id":471955,"name":"Statics and Dynamics","url":"https://www.academia.edu/Documents/in/Statics_and_Dynamics?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32796983" data-work_id="32796983" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32796983/Clustering_using_Unsupervised_Binary_Trees_CUBT">Clustering using Unsupervised Binary Trees: CUBT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32796983" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32796983" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2d455b7050c911ec04455493602ab405" rel="nofollow" data-download="{"attachment_id":52949489,"asset_id":32796983,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52949489/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32220926" href="https://independent.academia.edu/MarcelaSvarc">Marcela Svarc</a><script data-card-contents-for-user="32220926" type="text/json">{"id":32220926,"first_name":"Marcela","last_name":"Svarc","domain_name":"independent","page_name":"MarcelaSvarc","display_name":"Marcela Svarc","profile_url":"https://independent.academia.edu/MarcelaSvarc?f_ri=1351","photo":"https://0.academia-photos.com/32220926/28787766/26881427/s65_marcela.svarc.jpg"}</script></span></span></li><li class="js-paper-rank-work_32796983 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32796983"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32796983, container: ".js-paper-rank-work_32796983", }); 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$(".js-view-count[data-work-id=32796983]").text(description); $(".js-view-count-work_32796983").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_32796983").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="32796983"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="102722" rel="nofollow" href="https://www.academia.edu/Documents/in/Science_Learning">Science Learning</a>, <script data-card-contents-for-ri="102722" type="text/json">{"id":102722,"name":"Science Learning","url":"https://www.academia.edu/Documents/in/Science_Learning?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="327126" rel="nofollow" href="https://www.academia.edu/Documents/in/Clustering_Method">Clustering Method</a>, <script data-card-contents-for-ri="327126" type="text/json">{"id":327126,"name":"Clustering Method","url":"https://www.academia.edu/Documents/in/Clustering_Method?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="550543" rel="nofollow" href="https://www.academia.edu/Documents/in/Binary_Tree">Binary Tree</a><script data-card-contents-for-ri="550543" type="text/json">{"id":550543,"name":"Binary Tree","url":"https://www.academia.edu/Documents/in/Binary_Tree?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=32796983]'), work: {"id":32796983,"title":"Clustering using Unsupervised Binary Trees: CUBT","created_at":"2017-05-03T04:51:51.304-07:00","url":"https://www.academia.edu/32796983/Clustering_using_Unsupervised_Binary_Trees_CUBT?f_ri=1351","dom_id":"work_32796983","summary":"We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.","downloadable_attachments":[{"id":52949489,"asset_id":32796983,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32220926,"first_name":"Marcela","last_name":"Svarc","domain_name":"independent","page_name":"MarcelaSvarc","display_name":"Marcela Svarc","profile_url":"https://independent.academia.edu/MarcelaSvarc?f_ri=1351","photo":"https://0.academia-photos.com/32220926/28787766/26881427/s65_marcela.svarc.jpg"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":102722,"name":"Science Learning","url":"https://www.academia.edu/Documents/in/Science_Learning?f_ri=1351","nofollow":true},{"id":327126,"name":"Clustering Method","url":"https://www.academia.edu/Documents/in/Clustering_Method?f_ri=1351","nofollow":true},{"id":550543,"name":"Binary Tree","url":"https://www.academia.edu/Documents/in/Binary_Tree?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7435814" data-work_id="7435814" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7435814/Basic_Concepts_for_Documenting_SAS_Projects_Documentation_Styles_for_SAS_Projects_Programs_and_Variables">Basic Concepts for Documenting SAS® Projects: Documentation Styles for SAS Projects, Programs, and Variables. </a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper kicks off a project to write a comprehensive book of best practices for documenting SAS® projects. The presenter’s existing documentation styles are explained. The presenter wants to discuss and gather current best practices... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7435814" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper kicks off a project to write a comprehensive book of best practices for documenting SAS® projects. The presenter’s existing documentation styles are explained. The presenter wants to discuss and gather current best practices used by the SAS user community. The presenter shows documentation styles at three different levels of scope. The first is a style used for project documentation, the second a style for program documentation, and the third a style for variable documentation. This third style enables researchers to repeat the modeling in SAS research, in an alternative language, or conceptually.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7435814" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="38c4abf8dfdd950449afc5a3901957cc" rel="nofollow" data-download="{"attachment_id":34018949,"asset_id":7435814,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34018949/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2196" href="https://uottawa.academia.edu/PeterTimusk">Peter Timusk</a><script data-card-contents-for-user="2196" type="text/json">{"id":2196,"first_name":"Peter","last_name":"Timusk","domain_name":"uottawa","page_name":"PeterTimusk","display_name":"Peter Timusk","profile_url":"https://uottawa.academia.edu/PeterTimusk?f_ri=1351","photo":"https://0.academia-photos.com/2196/1039/1142/s65_peter.timusk.jpg"}</script></span></span></li><li class="js-paper-rank-work_7435814 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7435814"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7435814, container: ".js-paper-rank-work_7435814", }); 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$(".js-view-count[data-work-id=7435814]").text(description); $(".js-view-count-work_7435814").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_7435814").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="7435814"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2725" rel="nofollow" href="https://www.academia.edu/Documents/in/Documentation">Documentation</a>, <script data-card-contents-for-ri="2725" type="text/json">{"id":2725,"name":"Documentation","url":"https://www.academia.edu/Documents/in/Documentation?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Statistics">Applied Statistics</a><script data-card-contents-for-ri="4060" type="text/json">{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7435814]'), work: {"id":7435814,"title":"Basic Concepts for Documenting SAS® Projects: Documentation Styles for SAS Projects, Programs, and Variables. ","created_at":"2014-06-23T09:50:00.162-07:00","url":"https://www.academia.edu/7435814/Basic_Concepts_for_Documenting_SAS_Projects_Documentation_Styles_for_SAS_Projects_Programs_and_Variables?f_ri=1351","dom_id":"work_7435814","summary":"This paper kicks off a project to write a comprehensive book of best practices for documenting SAS® projects. The presenter’s existing documentation styles are explained. The presenter wants to discuss and gather current best practices used by the SAS user community. The presenter shows documentation styles at three different levels of scope. The first is a style used for project documentation, the second a style for program documentation, and the third a style for variable documentation. This third style enables researchers to repeat the modeling in SAS research, in an alternative language, or conceptually.","downloadable_attachments":[{"id":34018949,"asset_id":7435814,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2196,"first_name":"Peter","last_name":"Timusk","domain_name":"uottawa","page_name":"PeterTimusk","display_name":"Peter Timusk","profile_url":"https://uottawa.academia.edu/PeterTimusk?f_ri=1351","photo":"https://0.academia-photos.com/2196/1039/1142/s65_peter.timusk.jpg"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":2725,"name":"Documentation","url":"https://www.academia.edu/Documents/in/Documentation?f_ri=1351","nofollow":true},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=1351","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36086369" data-work_id="36086369" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36086369/We_exist_in_the_world_of_uncertainties_introductory_version_">We exist in the world of uncertainties (introductory version)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We exist in the world of uncertainties. Any uncertainty always arises from a conflict of experience and chance, more precisely, from a conflict between the observer's experience and the chance observation. In other words, this indivisible... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36086369" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We exist in the world of uncertainties.<br />Any uncertainty always arises from a conflict of experience and chance, more precisely, from a conflict between the observer's experience and the chance observation. In other words, this indivisible pair, ``experience and chance'', is the source of any uncertainty.<br />Other sources of uncertainty simply do not exist.<br />A theory that describes this conflict strictly mathematically is the new theory, called theory of co~events, or the theory of experience and chance. Now we can say that this theory is a theory of uncertainty in its broadest sense.<br /><br />The eventology approach gave impetus to the development of a theory that turned out to be broader than the theory of probabilities.<br />This new theory is a dual combination of two theories - Kolmogorov's theory of probabilities of ket-events (k-e.'s) and its dual reflection - a new theory of believabilities of bra-events (b-e.'s). Today, the theory of co~events has a strict axiomatics, in which Kolmogorov k-e.'s describing the future case of observation are dually reflected in b-e.'s describing the past experience of the observer, and a co~event is defined as a measurable binary relation on the Cartesian product: ``a set of bra-events X a set of terraced ket-events''.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36086369" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="573086e5ff90ed8041585d0dcdfb28fe" rel="nofollow" data-download="{"attachment_id":56089129,"asset_id":36086369,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56089129/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38890" href="https://sfu-kras.academia.edu/OlegVorobyev">Oleg Yu Vorobyev</a><script data-card-contents-for-user="38890" type="text/json">{"id":38890,"first_name":"Oleg Yu","last_name":"Vorobyev","domain_name":"sfu-kras","page_name":"OlegVorobyev","display_name":"Oleg Yu Vorobyev","profile_url":"https://sfu-kras.academia.edu/OlegVorobyev?f_ri=1351","photo":"https://0.academia-photos.com/38890/12977/347919/s65_oleg.vorobyev.gif"}</script></span></span></li><li class="js-paper-rank-work_36086369 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36086369"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36086369, container: ".js-paper-rank-work_36086369", }); 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In other words, this indivisible pair, ``experience and chance'', is the source of any uncertainty.\nOther sources of uncertainty simply do not exist.\nA theory that describes this conflict strictly mathematically is the new theory, called theory of co~events, or the theory of experience and chance. Now we can say that this theory is a theory of uncertainty in its broadest sense.\n\nThe eventology approach gave impetus to the development of a theory that turned out to be broader than the theory of probabilities.\nThis new theory is a dual combination of two theories - Kolmogorov's theory of probabilities of ket-events (k-e.'s) and its dual reflection - a new theory of believabilities of bra-events (b-e.'s). 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Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=1351"},{"id":1223686,"name":"Artificial Intelligent and Soft Computing Methodologies","url":"https://www.academia.edu/Documents/in/Artificial_Intelligent_and_Soft_Computing_Methodologies?f_ri=1351"},{"id":1317759,"name":"Metode Naive Bayes","url":"https://www.academia.edu/Documents/in/Metode_Naive_Bayes?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26832102" data-work_id="26832102" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26832102/Single_layer_solar_drying_behaviour_of_Citrus_aurantium_leaves_under_forced_convection">Single layer solar drying behaviour of Citrus aurantium leaves under forced convection</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Convective solar drying experiments in thin layers of Citrus aurantium leaves grown in Marrakech, morocco, were conducted. An indirect forced convection solar dryer consisting of a solar air collector, an auxiliary heater, a circulation... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26832102" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Convective solar drying experiments in thin layers of Citrus aurantium leaves grown in Marrakech, morocco, were conducted. An indirect forced convection solar dryer consisting of a solar air collector, an auxiliary heater, a circulation fan and a drying cabinet is used for the experiments. The air temperature was varied from 50 to 60°C; the relative humidity from 41% to 53%; and the drying air flow rate from 0.0277 to 0.0833 m 3 /s. Thirteen statistical models, which are semi-theoretical and/or empirical, were tested for fitting the experimental data. A nonlinear regression analysis using a statistical computer program was used to evaluate the constants of the models. The Midilli-Kucuk drying model was found to be the most suitable for describing the solar drying curves of Citrus aurantium leaves with a correlation coefficient (r) of 0.99998, chi-square (v 2 ) of 4.664 · 10 À6 and MBE of 4.8381 · 10 À4 .</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26832102" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7bdce892759632772cad4453a00f8b7f" rel="nofollow" data-download="{"attachment_id":47104669,"asset_id":26832102,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47104669/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31473" href="https://king-saud.academia.edu/SihamLahsasni">Siham Lahsasni</a><script data-card-contents-for-user="31473" type="text/json">{"id":31473,"first_name":"Siham","last_name":"Lahsasni","domain_name":"king-saud","page_name":"SihamLahsasni","display_name":"Siham Lahsasni","profile_url":"https://king-saud.academia.edu/SihamLahsasni?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_26832102 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="26832102"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 26832102, container: ".js-paper-rank-work_26832102", }); 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An indirect forced convection solar dryer consisting of a solar air collector, an auxiliary heater, a circulation fan and a drying cabinet is used for the experiments. The air temperature was varied from 50 to 60°C; the relative humidity from 41% to 53%; and the drying air flow rate from 0.0277 to 0.0833 m 3 /s. Thirteen statistical models, which are semi-theoretical and/or empirical, were tested for fitting the experimental data. A nonlinear regression analysis using a statistical computer program was used to evaluate the constants of the models. The Midilli-Kucuk drying model was found to be the most suitable for describing the solar drying curves of Citrus aurantium leaves with a correlation coefficient (r) of 0.99998, chi-square (v 2 ) of 4.664 · 10 À6 and MBE of 4.8381 · 10 À4 .","downloadable_attachments":[{"id":47104669,"asset_id":26832102,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31473,"first_name":"Siham","last_name":"Lahsasni","domain_name":"king-saud","page_name":"SihamLahsasni","display_name":"Siham Lahsasni","profile_url":"https://king-saud.academia.edu/SihamLahsasni?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":16682,"name":"Mathematical Modelling","url":"https://www.academia.edu/Documents/in/Mathematical_Modelling?f_ri=1351","nofollow":true},{"id":62729,"name":"Air flow","url":"https://www.academia.edu/Documents/in/Air_flow?f_ri=1351","nofollow":true},{"id":86410,"name":"Nonlinear Regression","url":"https://www.academia.edu/Documents/in/Nonlinear_Regression?f_ri=1351","nofollow":true},{"id":230744,"name":"Relative Humidity","url":"https://www.academia.edu/Documents/in/Relative_Humidity?f_ri=1351"},{"id":444096,"name":"Air Temperature","url":"https://www.academia.edu/Documents/in/Air_Temperature?f_ri=1351"},{"id":611814,"name":"Correlation coefficient","url":"https://www.academia.edu/Documents/in/Correlation_coefficient?f_ri=1351"},{"id":664700,"name":"Statistical Model","url":"https://www.academia.edu/Documents/in/Statistical_Model?f_ri=1351"},{"id":890685,"name":"Forced Convection","url":"https://www.academia.edu/Documents/in/Forced_Convection?f_ri=1351"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1351"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=1351"},{"id":2463717,"name":"Thin Layer","url":"https://www.academia.edu/Documents/in/Thin_Layer?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50633887" data-work_id="50633887" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50633887/General_Purpose_Convolution_Algorithm_in_S_4_Classes_by_Means_of_FFT">General Purpose Convolution Algorithm in S 4 Classes by Means of FFT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Object orientation provides a flexible framework for the implementation of the convolution of arbitrary distributions of real-valued random variables. We discuss an algorithm which is based on the Discrete Fourier Transformation and its... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50633887" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Object orientation provides a flexible framework for the implementation of the convolution of arbitrary distributions of real-valued random variables. We discuss an algorithm which is based on the Discrete Fourier Transformation and its fast computability via the Fast Fourier Transformation. It directly applies to lattice-supported distributions. In the case of continuous distributions an additional discretization to a linear lattice is necessary and the resulting lattice-supported distributions are suitably smoothed after convolution. Our focus is on R package distr which includes an implementation of this approach. Several checks in situations where the exact results are known confirm a high accuracy of the proposed algorithm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50633887" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9b00692b21cd42cb97f4e15e80fb8e83" rel="nofollow" data-download="{"attachment_id":68542670,"asset_id":50633887,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68542670/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="197431157" href="https://independent.academia.edu/kohlmatthias">matthias kohl</a><script data-card-contents-for-user="197431157" type="text/json">{"id":197431157,"first_name":"matthias","last_name":"kohl","domain_name":"independent","page_name":"kohlmatthias","display_name":"matthias kohl","profile_url":"https://independent.academia.edu/kohlmatthias?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_50633887 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50633887"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50633887, container: ".js-paper-rank-work_50633887", }); 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$(".js-view-count[data-work-id=50633887]").text(description); $(".js-view-count-work_50633887").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_50633887").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50633887"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="198462" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_software">Statistical software</a>, <script data-card-contents-for-ri="198462" type="text/json">{"id":198462,"name":"Statistical software","url":"https://www.academia.edu/Documents/in/Statistical_software?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="588226" rel="nofollow" href="https://www.academia.edu/Documents/in/Fast_Fourier_Transform">Fast Fourier Transform</a><script data-card-contents-for-ri="588226" type="text/json">{"id":588226,"name":"Fast Fourier Transform","url":"https://www.academia.edu/Documents/in/Fast_Fourier_Transform?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50633887]'), work: {"id":50633887,"title":"General Purpose Convolution Algorithm in S 4 Classes by Means of FFT","created_at":"2021-08-03T04:30:10.429-07:00","url":"https://www.academia.edu/50633887/General_Purpose_Convolution_Algorithm_in_S_4_Classes_by_Means_of_FFT?f_ri=1351","dom_id":"work_50633887","summary":"Object orientation provides a flexible framework for the implementation of the convolution of arbitrary distributions of real-valued random variables. We discuss an algorithm which is based on the Discrete Fourier Transformation and its fast computability via the Fast Fourier Transformation. It directly applies to lattice-supported distributions. In the case of continuous distributions an additional discretization to a linear lattice is necessary and the resulting lattice-supported distributions are suitably smoothed after convolution. Our focus is on R package distr which includes an implementation of this approach. Several checks in situations where the exact results are known confirm a high accuracy of the proposed algorithm.","downloadable_attachments":[{"id":68542670,"asset_id":50633887,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":197431157,"first_name":"matthias","last_name":"kohl","domain_name":"independent","page_name":"kohlmatthias","display_name":"matthias kohl","profile_url":"https://independent.academia.edu/kohlmatthias?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":198462,"name":"Statistical software","url":"https://www.academia.edu/Documents/in/Statistical_software?f_ri=1351","nofollow":true},{"id":588226,"name":"Fast Fourier Transform","url":"https://www.academia.edu/Documents/in/Fast_Fourier_Transform?f_ri=1351","nofollow":true},{"id":611819,"name":"Discrete random variable","url":"https://www.academia.edu/Documents/in/Discrete_random_variable?f_ri=1351"},{"id":1121048,"name":"Object Oriented","url":"https://www.academia.edu/Documents/in/Object_Oriented?f_ri=1351"},{"id":1800838,"name":"Discrete Fourier Transform","url":"https://www.academia.edu/Documents/in/Discrete_Fourier_Transform?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34434980 coauthored" data-work_id="34434980" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34434980/Short_Term_Wind_Power_Forecasting_Based_On_Quantile_Regression">Short-Term Wind Power Forecasting Based On Quantile Regression</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Short-term wind power forecasts are fundamental information for the safe and economic integration of wind farms into an electric power system. In this work we present a Generalized Additive Model to predict the wind power quantiles... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34434980" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Short-term wind power forecasts are fundamental information for the safe and economic integration of wind farms into an electric power system. In this work we present a Generalized Additive Model to predict the wind power quantiles (Quantile Regression) from which we obtain a prediction of the wind power production probability density function in a wind farm. The methodology was implemented in the VENTOS Program. In order to illustrate the application of the methodology as well as the VENTOS Program this work presents the results achieved by a computational experiment based on real data from a wind farm located in Galicia, Spain.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34434980" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c2496635ca6ac28ca165b44915c13f5b" rel="nofollow" data-download="{"attachment_id":54310413,"asset_id":34434980,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54310413/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1957474" href="https://uerj.academia.edu/Jos%C3%A9Pessanha">José Francisco Pessanha</a><script data-card-contents-for-user="1957474" type="text/json">{"id":1957474,"first_name":"José Francisco","last_name":"Pessanha","domain_name":"uerj","page_name":"JoséPessanha","display_name":"José Francisco Pessanha","profile_url":"https://uerj.academia.edu/Jos%C3%A9Pessanha?f_ri=1351","photo":"https://0.academia-photos.com/1957474/2757869/14606900/s65_jos_francisco.pessanha.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-34434980">+2</span><div class="hidden js-additional-users-34434980"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/ValkCastellani">Valk Castellani</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/VictorAAlmeida">Victor. A. Almeida</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-34434980'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-34434980').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_34434980 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34434980"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34434980, container: ".js-paper-rank-work_34434980", }); });</script></li><li class="js-percentile-work_34434980 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34434980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_34434980"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_34434980 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="34434980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34434980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34434980]").text(description); $(".js-view-count-work_34434980").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34434980").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34434980"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">40</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="49" rel="nofollow" href="https://www.academia.edu/Documents/in/Electrical_Engineering">Electrical Engineering</a>, <script data-card-contents-for-ri="49" type="text/json">{"id":49,"name":"Electrical Engineering","url":"https://www.academia.edu/Documents/in/Electrical_Engineering?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2738" rel="nofollow" href="https://www.academia.edu/Documents/in/Renewable_Energy">Renewable Energy</a><script data-card-contents-for-ri="2738" type="text/json">{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34434980]'), work: {"id":34434980,"title":"Short-Term Wind Power Forecasting Based On Quantile Regression","created_at":"2017-09-01T06:01:51.309-07:00","url":"https://www.academia.edu/34434980/Short_Term_Wind_Power_Forecasting_Based_On_Quantile_Regression?f_ri=1351","dom_id":"work_34434980","summary":"Short-term wind power forecasts are fundamental information for the safe and economic integration of wind farms into an electric power system. In this work we present a Generalized Additive Model to predict the wind power quantiles (Quantile Regression) from which we obtain a prediction of the wind power production probability density function in a wind farm. The methodology was implemented in the VENTOS Program. In order to illustrate the application of the methodology as well as the VENTOS Program this work presents the results achieved by a computational experiment based on real data from a wind farm located in Galicia, Spain.","downloadable_attachments":[{"id":54310413,"asset_id":34434980,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1957474,"first_name":"José Francisco","last_name":"Pessanha","domain_name":"uerj","page_name":"JoséPessanha","display_name":"José Francisco Pessanha","profile_url":"https://uerj.academia.edu/Jos%C3%A9Pessanha?f_ri=1351","photo":"https://0.academia-photos.com/1957474/2757869/14606900/s65_jos_francisco.pessanha.jpg"},{"id":13530943,"first_name":"Valk","last_name":"Castellani","domain_name":"independent","page_name":"ValkCastellani","display_name":"Valk Castellani","profile_url":"https://independent.academia.edu/ValkCastellani?f_ri=1351","photo":"https://0.academia-photos.com/13530943/13082482/14410144/s65_valk.castellani.jpg"},{"id":59707239,"first_name":"Victor. A.","last_name":"Almeida","domain_name":"independent","page_name":"VictorAAlmeida","display_name":"Victor. A. 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href="https://www.academia.edu/15364347/A_Bayesian_framework_for_analyzing_iEEG_data_from_a_rat_model_of_epilepsy">A Bayesian framework for analyzing iEEG data from a rat model of epilepsy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The early detection of epileptic seizures requires computing relevant statistics from multivariate data and defining a robust decision strategy as a function of these statistics that accurately detects the transition from the normal to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15364347" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The early detection of epileptic seizures requires computing relevant statistics from multivariate data and defining a robust decision strategy as a function of these statistics that accurately detects the transition from the normal to the peri-ictal (problematic) state. We model the afflicted brain as a hidden Markov model (HMM) with two hidden clinical states (normal and peri-ictal). The output of the HMM is a statistic computed from multivariate neural measurements. A Bayesian framework is developed to analyze the a posteriori conditional probability of being in peri-ictal state given current and past output measurements. We apply this method to multichannel intracortical EEGs (iEEGs) from the thalamo-cortical ictal pathway in an epilepsy rat model. We first define the output statistic as the max singular value of a connectivity matrix computed on the EEG channels with spectral techniques Then, we estimate the HMM transition probabilities from this statistic and track the a poste...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15364347" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fa043fe3cb4eb0036383169a4670767d" rel="nofollow" data-download="{"attachment_id":43292215,"asset_id":15364347,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43292215/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34507257" href="https://johnshopkins.academia.edu/SrideviSarma">Sridevi Sarma</a><script data-card-contents-for-user="34507257" type="text/json">{"id":34507257,"first_name":"Sridevi","last_name":"Sarma","domain_name":"johnshopkins","page_name":"SrideviSarma","display_name":"Sridevi Sarma","profile_url":"https://johnshopkins.academia.edu/SrideviSarma?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15364347">+1</span><div class="hidden js-additional-users-15364347"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MarekMirski">Marek Mirski</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15364347'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15364347').html(); 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We model the afflicted brain as a hidden Markov model (HMM) with two hidden clinical states (normal and peri-ictal). The output of the HMM is a statistic computed from multivariate neural measurements. A Bayesian framework is developed to analyze the a posteriori conditional probability of being in peri-ictal state given current and past output measurements. We apply this method to multichannel intracortical EEGs (iEEGs) from the thalamo-cortical ictal pathway in an epilepsy rat model. We first define the output statistic as the max singular value of a connectivity matrix computed on the EEG channels with spectral techniques Then, we estimate the HMM transition probabilities from this statistic and track the a poste...","downloadable_attachments":[{"id":43292215,"asset_id":15364347,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34507257,"first_name":"Sridevi","last_name":"Sarma","domain_name":"johnshopkins","page_name":"SrideviSarma","display_name":"Sridevi Sarma","profile_url":"https://johnshopkins.academia.edu/SrideviSarma?f_ri=1351","photo":"/images/s65_no_pic.png"},{"id":41053241,"first_name":"Marek","last_name":"Mirski","domain_name":"independent","page_name":"MarekMirski","display_name":"Marek Mirski","profile_url":"https://independent.academia.edu/MarekMirski?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":7648,"name":"Epilepsy","url":"https://www.academia.edu/Documents/in/Epilepsy?f_ri=1351","nofollow":true},{"id":10904,"name":"Electroencephalography","url":"https://www.academia.edu/Documents/in/Electroencephalography?f_ri=1351","nofollow":true},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model?f_ri=1351"},{"id":207885,"name":"Conditional probability","url":"https://www.academia.edu/Documents/in/Conditional_probability?f_ri=1351"},{"id":371528,"name":"Early Detection","url":"https://www.academia.edu/Documents/in/Early_Detection?f_ri=1351"},{"id":375054,"name":"Rats","url":"https://www.academia.edu/Documents/in/Rats?f_ri=1351"},{"id":404251,"name":"Multivariate Data","url":"https://www.academia.edu/Documents/in/Multivariate_Data?f_ri=1351"},{"id":549280,"name":"Reproducibility of Results","url":"https://www.academia.edu/Documents/in/Reproducibility_of_Results?f_ri=1351"},{"id":732354,"name":"Rat Model","url":"https://www.academia.edu/Documents/in/Rat_Model?f_ri=1351"},{"id":880279,"name":"Bayes Theorem","url":"https://www.academia.edu/Documents/in/Bayes_Theorem-1?f_ri=1351"},{"id":901876,"name":"Sensitivity and Specificity","url":"https://www.academia.edu/Documents/in/Sensitivity_and_Specificity?f_ri=1351"},{"id":1568702,"name":"Delay Estimation","url":"https://www.academia.edu/Documents/in/Delay_Estimation?f_ri=1351"},{"id":1788300,"name":"Bayesian framework","url":"https://www.academia.edu/Documents/in/Bayesian_framework?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74798080" data-work_id="74798080" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/74798080/Zero_Variance_Markov_Chain_Monte_Carlo_for_Bayesian_Estimators">Zero Variance Markov Chain Monte Carlo for Bayesian Estimators</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74798080" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed. Conditions for asymptotic unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the idea is illustrated with real applications to probit, logit and GARCH Bayesian models. For all these models, a central limit theorem and unbiasedness for the zero-variance estimator are proved (see the supplementary material available on-line).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74798080" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0f1aaa58e84095428914f7c200758eab" rel="nofollow" data-download="{"attachment_id":82818905,"asset_id":74798080,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82818905/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32843285" href="https://independent.academia.edu/AntoniettaMira">Antonietta Mira</a><script data-card-contents-for-user="32843285" type="text/json">{"id":32843285,"first_name":"Antonietta","last_name":"Mira","domain_name":"independent","page_name":"AntoniettaMira","display_name":"Antonietta Mira","profile_url":"https://independent.academia.edu/AntoniettaMira?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_74798080 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74798080"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74798080, container: ".js-paper-rank-work_74798080", }); 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$(".js-view-count[data-work-id=74798080]").text(description); $(".js-view-count-work_74798080").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_74798080").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="74798080"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a><script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=74798080]'), work: {"id":74798080,"title":"Zero Variance Markov Chain Monte Carlo for Bayesian Estimators","created_at":"2022-03-28T07:26:54.085-07:00","url":"https://www.academia.edu/74798080/Zero_Variance_Markov_Chain_Monte_Carlo_for_Bayesian_Estimators?f_ri=1351","dom_id":"work_74798080","summary":"Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed. Conditions for asymptotic unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the idea is illustrated with real applications to probit, logit and GARCH Bayesian models. For all these models, a central limit theorem and unbiasedness for the zero-variance estimator are proved (see the supplementary material available on-line).","downloadable_attachments":[{"id":82818905,"asset_id":74798080,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32843285,"first_name":"Antonietta","last_name":"Mira","domain_name":"independent","page_name":"AntoniettaMira","display_name":"Antonietta Mira","profile_url":"https://independent.academia.edu/AntoniettaMira?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1351","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1351","nofollow":true},{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":4392,"name":"Monte Carlo Simulation","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Simulation?f_ri=1351"},{"id":26903,"name":"Quantitative Methods in Economics","url":"https://www.academia.edu/Documents/in/Quantitative_Methods_in_Economics?f_ri=1351"},{"id":32433,"name":"Logistic Regression","url":"https://www.academia.edu/Documents/in/Logistic_Regression?f_ri=1351"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1351"},{"id":67959,"name":"Probability Distribution \u0026 Applications","url":"https://www.academia.edu/Documents/in/Probability_Distribution_and_Applications?f_ri=1351"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=1351"},{"id":285582,"name":"Garch Models","url":"https://www.academia.edu/Documents/in/Garch_Models?f_ri=1351"},{"id":1346336,"name":"Variance Reduction Techniques","url":"https://www.academia.edu/Documents/in/Variance_Reduction_Techniques?f_ri=1351"},{"id":1434720,"name":"Central Limit Theorem","url":"https://www.academia.edu/Documents/in/Central_Limit_Theorem?f_ri=1351"},{"id":1951089,"name":"Bayesian Estimator","url":"https://www.academia.edu/Documents/in/Bayesian_Estimator?f_ri=1351"},{"id":1970694,"name":"Variance Estimation","url":"https://www.academia.edu/Documents/in/Variance_Estimation?f_ri=1351"},{"id":2485951,"name":"Posterior distribution","url":"https://www.academia.edu/Documents/in/Posterior_distribution?f_ri=1351"},{"id":2814568,"name":"Probability Distribution","url":"https://www.academia.edu/Documents/in/Probability_Distribution?f_ri=1351"},{"id":3489303,"name":"GARCH Model","url":"https://www.academia.edu/Documents/in/GARCH_Model?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16087376" data-work_id="16087376" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16087376/Asymptotically_Independent_Markov_Sampling_a_new_MCMC_scheme_for_Bayesian_Inference">Asymptotically Independent Markov Sampling: a new MCMC scheme for Bayesian Inference</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In Bayesian statistics, many problems can be expressed as the evaluation of the expectation of a quantity of interest with respect to the posterior distribution. Standard Monte Carlo method is often not applicable because the encountered... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16087376" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In Bayesian statistics, many problems can be expressed as the evaluation of the expectation of a quantity of interest with respect to the posterior distribution. Standard Monte Carlo method is often not applicable because the encountered posterior distributions cannot be sampled directly. In this case, the most popular strategies are the importance sampling method, Markov chain Monte Carlo, and annealing.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16087376" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f3434a4aede5a87e037d76941e41ac55" rel="nofollow" data-download="{"attachment_id":38867881,"asset_id":16087376,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38867881/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35210931" href="https://independent.academia.edu/KonstantinMZuev">Konstantin M. Zuev</a><script data-card-contents-for-user="35210931" type="text/json">{"id":35210931,"first_name":"Konstantin M.","last_name":"Zuev","domain_name":"independent","page_name":"KonstantinMZuev","display_name":"Konstantin M. Zuev","profile_url":"https://independent.academia.edu/KonstantinMZuev?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_16087376 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16087376"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16087376, container: ".js-paper-rank-work_16087376", }); });</script></li><li class="js-percentile-work_16087376 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16087376; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_16087376"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_16087376 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="16087376"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16087376; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16087376]").text(description); $(".js-view-count-work_16087376").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_16087376").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="16087376"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1351" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Computing">Statistical Computing</a>, <script data-card-contents-for-ri="1351" type="text/json">{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="15084" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_machine_learning">Statistical machine learning</a>, <script data-card-contents-for-ri="15084" type="text/json">{"id":15084,"name":"Statistical machine learning","url":"https://www.academia.edu/Documents/in/Statistical_machine_learning?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1351","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="85262" rel="nofollow" href="https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo">Markov Chain Monte Carlo</a><script data-card-contents-for-ri="85262" type="text/json">{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=1351","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=16087376]'), work: {"id":16087376,"title":"Asymptotically Independent Markov Sampling: a new MCMC scheme for Bayesian Inference","created_at":"2015-09-23T08:59:04.777-07:00","url":"https://www.academia.edu/16087376/Asymptotically_Independent_Markov_Sampling_a_new_MCMC_scheme_for_Bayesian_Inference?f_ri=1351","dom_id":"work_16087376","summary":"In Bayesian statistics, many problems can be expressed as the evaluation of the expectation of a quantity of interest with respect to the posterior distribution. Standard Monte Carlo method is often not applicable because the encountered posterior distributions cannot be sampled directly. In this case, the most popular strategies are the importance sampling method, Markov chain Monte Carlo, and annealing.","downloadable_attachments":[{"id":38867881,"asset_id":16087376,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35210931,"first_name":"Konstantin M.","last_name":"Zuev","domain_name":"independent","page_name":"KonstantinMZuev","display_name":"Konstantin M. Zuev","profile_url":"https://independent.academia.edu/KonstantinMZuev?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":15084,"name":"Statistical machine learning","url":"https://www.academia.edu/Documents/in/Statistical_machine_learning?f_ri=1351","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1351","nofollow":true},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=1351","nofollow":true},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=1351"},{"id":307221,"name":"Importance Sampling","url":"https://www.academia.edu/Documents/in/Importance_Sampling?f_ri=1351"},{"id":595993,"name":"Markov chain","url":"https://www.academia.edu/Documents/in/Markov_chain?f_ri=1351"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16722383" data-work_id="16722383" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16722383/A_tutorial_introduction_to_the_minimum_description_length_principle">A tutorial introduction to the minimum description length principle</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16722383" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the first chapter are made mathematically precise. This tutorial will appear as the first two chapters in the collection Advances in Minimum Description Length: Theory and Applications [Grünwald, Myung, and Pitt 2004], to be published by the MIT Press.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16722383" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0240d399155677317532b3c262c75fe1" rel="nofollow" data-download="{"attachment_id":39146720,"asset_id":16722383,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39146720/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33063386" href="https://leidenuniv.academia.edu/PeterGr%C3%BCnwald">Peter Grünwald</a><script data-card-contents-for-user="33063386" type="text/json">{"id":33063386,"first_name":"Peter","last_name":"Grünwald","domain_name":"leidenuniv","page_name":"PeterGrünwald","display_name":"Peter Grünwald","profile_url":"https://leidenuniv.academia.edu/PeterGr%C3%BCnwald?f_ri=1351","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_16722383 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16722383"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16722383, container: ".js-paper-rank-work_16722383", }); 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The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the first chapter are made mathematically precise. This tutorial will appear as the first two chapters in the collection Advances in Minimum Description Length: Theory and Applications [Grünwald, Myung, and Pitt 2004], to be published by the MIT Press.","downloadable_attachments":[{"id":39146720,"asset_id":16722383,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33063386,"first_name":"Peter","last_name":"Grünwald","domain_name":"leidenuniv","page_name":"PeterGrünwald","display_name":"Peter Grünwald","profile_url":"https://leidenuniv.academia.edu/PeterGr%C3%BCnwald?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical Computing","url":"https://www.academia.edu/Documents/in/Statistical_Computing?f_ri=1351","nofollow":true},{"id":1410,"name":"Information Theory","url":"https://www.academia.edu/Documents/in/Information_Theory?f_ri=1351","nofollow":true},{"id":102722,"name":"Science Learning","url":"https://www.academia.edu/Documents/in/Science_Learning?f_ri=1351","nofollow":true},{"id":287030,"name":"Minimum description length","url":"https://www.academia.edu/Documents/in/Minimum_description_length?f_ri=1351","nofollow":true},{"id":1675981,"name":"Minimum Description Length Principle","url":"https://www.academia.edu/Documents/in/Minimum_Description_Length_Principle?f_ri=1351"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14449209" data-work_id="14449209" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14449209/The_Significance_Of_The_Non_Trivial_Zeros_Of_The_Riemann_Zeta_Function">The Significance Of The Non-Trivial Zeros Of The Riemann Zeta Function</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">This paper expounds the role of the non-trivial zeros of the Riemann zeta function ζ and supplements the author’s earlier papers on the Riemann hypothesis. There is a lot of mystery surrounding the non-trivial zeros.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14449209" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="24c8ce00e42c7c82753e34934e5ec951" rel="nofollow" data-download="{"attachment_id":38327855,"asset_id":14449209,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38327855/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11414130" href="https://independent.academia.edu/WongBertrand">Bertrand Wong</a><script data-card-contents-for-user="11414130" type="text/json">{"id":11414130,"first_name":"Bertrand","last_name":"Wong","domain_name":"independent","page_name":"WongBertrand","display_name":"Bertrand Wong","profile_url":"https://independent.academia.edu/WongBertrand?f_ri=1351","photo":"https://0.academia-photos.com/11414130/3329015/3917133/s65_bertrand.wong.jpg"}</script></span></span></li><li class="js-paper-rank-work_14449209 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14449209"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14449209, container: ".js-paper-rank-work_14449209", }); 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There is a lot of mystery surrounding the non-trivial zeros.","downloadable_attachments":[{"id":38327855,"asset_id":14449209,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11414130,"first_name":"Bertrand","last_name":"Wong","domain_name":"independent","page_name":"WongBertrand","display_name":"Bertrand Wong","profile_url":"https://independent.academia.edu/WongBertrand?f_ri=1351","photo":"https://0.academia-photos.com/11414130/3329015/3917133/s65_bertrand.wong.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1351","nofollow":true},{"id":301,"name":"Number Theory","url":"https://www.academia.edu/Documents/in/Number_Theory?f_ri=1351","nofollow":true},{"id":302,"name":"Analytic Number Theory","url":"https://www.academia.edu/Documents/in/Analytic_Number_Theory?f_ri=1351","nofollow":true},{"id":303,"name":"Algebraic Number 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js-work-link" href="https://www.academia.edu/13051742/Spatio_temporal_prediction_of_daily_temperatures_using_time_series_of_MODIS_LST_images">Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13051742" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1 • C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4 • C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement-interactive spacetime variogram exploration and automated retrieval, resampling and filtering of MODIS images-are anticipated.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13051742" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4edc6919af63f8d6dd44fd990464485f" rel="nofollow" data-download="{"attachment_id":45737110,"asset_id":13051742,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45737110/download_file?st=MTc0MDU3MjE4OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32287087" href="https://uni-muenster.academia.edu/EdzerPebesma">Edzer Pebesma</a><script data-card-contents-for-user="32287087" type="text/json">{"id":32287087,"first_name":"Edzer","last_name":"Pebesma","domain_name":"uni-muenster","page_name":"EdzerPebesma","display_name":"Edzer Pebesma","profile_url":"https://uni-muenster.academia.edu/EdzerPebesma?f_ri=1351","photo":"https://gravatar.com/avatar/0cb923f9e51793448b544f4be3fc5fd9?s=65"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-13051742">+1</span><div class="hidden js-additional-users-13051742"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://wur.academia.edu/GHeuvelink">Gerard Heuvelink</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-13051742'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-13051742').html(); 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8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1 • C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4 • C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement-interactive spacetime variogram exploration and automated retrieval, resampling and filtering of MODIS images-are anticipated.","downloadable_attachments":[{"id":45737110,"asset_id":13051742,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32287087,"first_name":"Edzer","last_name":"Pebesma","domain_name":"uni-muenster","page_name":"EdzerPebesma","display_name":"Edzer Pebesma","profile_url":"https://uni-muenster.academia.edu/EdzerPebesma?f_ri=1351","photo":"https://gravatar.com/avatar/0cb923f9e51793448b544f4be3fc5fd9?s=65"},{"id":19074417,"first_name":"Gerard","last_name":"Heuvelink","domain_name":"wur","page_name":"GHeuvelink","display_name":"Gerard Heuvelink","profile_url":"https://wur.academia.edu/GHeuvelink?f_ri=1351","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1351,"name":"Statistical 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