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distribution</title> <meta name="description" content="Search results for: statistical distribution"> <meta name="keywords" content="statistical distribution"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="statistical distribution"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8691</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: statistical distribution</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8691</span> A Proposed Mechanism for Skewing Symmetric Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title="normal distribution">normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=Fisher%20information" title=" Fisher information"> Fisher information</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20distributions" title=" symmetric distributions"> symmetric distributions</a> </p> <a href="https://publications.waset.org/abstracts/28593/a-proposed-mechanism-for-skewing-symmetric-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">659</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8690</span> Statistical Characteristics of Distribution of Radiation-Induced Defects under Random Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Selyshchev">P. Selyshchev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider fluctuations of defects density taking into account their interaction. Stochastic field of displacement generation rate gives random defect distribution. We determinate statistical characteristics (mean and dispersion) of random field of point defect distribution as function of defect generation parameters, temperature and properties of irradiated crystal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irradiation" title="irradiation">irradiation</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20defects" title=" primary defects"> primary defects</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction" title=" interaction"> interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuations" title=" fluctuations"> fluctuations</a> </p> <a href="https://publications.waset.org/abstracts/10105/statistical-characteristics-of-distribution-of-radiation-induced-defects-under-random-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10105.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">343</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8689</span> Implementation of Statistical Parameters to Form an Entropic Mathematical Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurcharan%20Singh%20Buttar">Gurcharan Singh Buttar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It has been discovered that although these two areas, statistics, and information theory, are independent in their nature, they can be combined to create applications in multidisciplinary mathematics. This is due to the fact that where in the field of statistics, statistical parameters (measures) play an essential role in reference to the population (distribution) under investigation. Information measure is crucial in the study of ambiguity, assortment, and unpredictability present in an array of phenomena. The following communication is a link between the two, and it has been demonstrated that the well-known conventional statistical measures can be used as a measure of information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=probability%20distribution" title="probability distribution">probability distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=concavity" title=" concavity"> concavity</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetry" title=" symmetry"> symmetry</a>, <a href="https://publications.waset.org/abstracts/search?q=variance" title=" variance"> variance</a>, <a href="https://publications.waset.org/abstracts/search?q=central%20tendency" title=" central tendency"> central tendency</a> </p> <a href="https://publications.waset.org/abstracts/142293/implementation-of-statistical-parameters-to-form-an-entropic-mathematical-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142293.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8688</span> Presenting a Model in the Analysis of Supply Chain Management Components by Using Statistical Distribution Functions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Rostamkhani">Ramin Rostamkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Thurasamy%20Ramayah"> Thurasamy Ramayah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analyzing" title="analyzing">analyzing</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20capability%20indices" title=" process capability indices"> process capability indices</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20distribution%20functions" title=" statistical distribution functions"> statistical distribution functions</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management%20components" title=" supply chain management components"> supply chain management components</a> </p> <a href="https://publications.waset.org/abstracts/155389/presenting-a-model-in-the-analysis-of-supply-chain-management-components-by-using-statistical-distribution-functions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155389.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">87</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8687</span> A Flexible Pareto Distribution Using α-Power Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shumaila%20Ehtisham">Shumaila Ehtisham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Statistical Distribution Theory, considering an additional parameter to classical distributions is a usual practice. In this study, a new distribution referred to as α-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution including explicit expressions for the moment generating function, mode, quantiles, entropies and order statistics are obtained. Unknown parameters have been estimated by using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that α-Power Pareto distribution outperforms while compared to different variants of Pareto distribution on the basis of model selection criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%CE%B1-power%20transformation" title="α-power transformation">α-power transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=moment%20generating%20function" title=" moment generating function"> moment generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20distribution" title=" Pareto distribution"> Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/89859/a-flexible-pareto-distribution-using-a-power-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8686</span> The Beta-Fisher Snedecor Distribution with Applications to Cancer Remission Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Adepoju">K. A. Adepoju</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20I.%20Shittu"> O. I. Shittu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20U.%20Chukwu"> A. U. Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, moment generating function and maximum likelihood estimation, as well as its Fisher information, were obtained. The flexibility of this distribution as well as its robustness using cancer remission time data was demonstrated. The new distribution can be used in most applications where the assumption underlying the use of other lifetime distributions is violated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fisher-snedecor%20distribution" title="fisher-snedecor distribution">fisher-snedecor distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-f%20distribution" title=" beta-f distribution"> beta-f distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20method" title=" maximum likelihood method"> maximum likelihood method</a> </p> <a href="https://publications.waset.org/abstracts/46554/the-beta-fisher-snedecor-distribution-with-applications-to-cancer-remission-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46554.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8685</span> Statistical Description of Counterpoise Effective Length Based on Regressive Formulas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petar%20Sarajcev">Petar Sarajcev</a>, <a href="https://publications.waset.org/abstracts/search?q=Josip%20Vasilj"> Josip Vasilj</a>, <a href="https://publications.waset.org/abstracts/search?q=Damir%20Jakus"> Damir Jakus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel statistical description of the counterpoise effective length due to lightning surges, where the (impulse) effective length had been obtained by means of regressive formulas applied to the transient simulation results. The effective length is described in terms of a statistical distribution function, from which median, mean, variance, and other parameters of interest could be readily obtained. The influence of lightning current amplitude, lightning front duration, and soil resistivity on the effective length has been accounted for, assuming statistical nature of these parameters. A method for determining the optimal counterpoise length, in terms of the statistical impulse effective length, is also presented. It is based on estimating the number of dangerous events associated with lightning strikes. Proposed statistical description and the associated method provide valuable information which could aid the design engineer in optimising physical lengths of counterpoises in different grounding arrangements and soil resistivity situations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=counterpoise" title="counterpoise">counterpoise</a>, <a href="https://publications.waset.org/abstracts/search?q=grounding%20conductor" title=" grounding conductor"> grounding conductor</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20length" title=" effective length"> effective length</a>, <a href="https://publications.waset.org/abstracts/search?q=lightning" title=" lightning"> lightning</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20method" title=" Monte Carlo method"> Monte Carlo method</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20distribution" title=" statistical distribution"> statistical distribution</a> </p> <a href="https://publications.waset.org/abstracts/16716/statistical-description-of-counterpoise-effective-length-based-on-regressive-formulas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8684</span> Statistical Analysis of Cables in Long-Span Cable-Stayed Bridges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ceshi%20Sun">Ceshi Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yueyu%20Zhao"> Yueyu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaobing%20Zhao"> Yaobing Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiqiang%20Wang"> Zhiqiang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Peng"> Jian Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Pengxin%20Guo"> Pengxin Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of transportation, there are more than 100 cable-stayed bridges with main span larger than 300 m in China. In order to ascertain the statistical relationships among the design parameters of stay cables and their distribution characteristics, 1500 cables were selected from 25 practical long-span cable-stayed bridges. A new relationship between the first order frequency and the length of cable was found by conducting the curve fitting. Then, based on this relationship other interesting relationships were deduced. Several probability density functions (PDFs) were used to investigate the distributions of the parameters of first order frequency, stress level and the Irvine parameter. It was found that these parameters obey the Lognormal distribution, the Weibull distribution and the generalized Pareto distribution, respectively. Scatter diagrams of the three parameters were plotted and their 95% confidence intervals were also investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cable" title="cable">cable</a>, <a href="https://publications.waset.org/abstracts/search?q=cable-stayed%20bridge" title=" cable-stayed bridge"> cable-stayed bridge</a>, <a href="https://publications.waset.org/abstracts/search?q=long-span" title=" long-span"> long-span</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/12878/statistical-analysis-of-cables-in-long-span-cable-stayed-bridges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12878.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">634</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8683</span> Statistical Convergence for the Approximation of Linear Positive Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Bhardwaj">Neha Bhardwaj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider positive linear operators and study the Voronovskaya type result of the operator then obtain an error estimate in terms of the higher order modulus of continuity of the function being approximated and its A-statistical convergence. Also, we compute the corresponding rate of A-statistical convergence for the linear positive operators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title="Poisson distribution">Poisson distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Voronovskaya" title=" Voronovskaya"> Voronovskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20of%20continuity" title=" modulus of continuity"> modulus of continuity</a>, <a href="https://publications.waset.org/abstracts/search?q=a-statistical%20convergence" title=" a-statistical convergence"> a-statistical convergence</a> </p> <a href="https://publications.waset.org/abstracts/70017/statistical-convergence-for-the-approximation-of-linear-positive-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8682</span> A Proposed Algorithm for Obtaining the Map of Subscribers’ Density Distribution for a Mobile Wireless Communication Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Temaneh-Nyah">C. Temaneh-Nyah</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20A.%20Phiri"> F. A. Phiri</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Karegeya"> D. Karegeya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an algorithm for obtaining the map of subscriber’s density distribution for a mobile wireless communication network based on the actual subscriber's traffic data obtained from the base station. This is useful in statistical characterization of the mobile wireless network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20compatibility" title="electromagnetic compatibility">electromagnetic compatibility</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20of%20communication%20network" title=" simulation of communication network"> simulation of communication network</a>, <a href="https://publications.waset.org/abstracts/search?q=subscriber%20density" title=" subscriber density"> subscriber density</a> </p> <a href="https://publications.waset.org/abstracts/2453/a-proposed-algorithm-for-obtaining-the-map-of-subscribers-density-distribution-for-a-mobile-wireless-communication-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2453.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8681</span> Monte Carlo Methods and Statistical Inference of Multitype Branching Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Staneva">Ana Staneva</a>, <a href="https://publications.waset.org/abstracts/search?q=Vessela%20Stoimenova"> Vessela Stoimenova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title="Bayesian">Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=branching%20processes" title=" branching processes"> branching processes</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20sampler" title=" Gibbs sampler"> Gibbs sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20methods" title=" Monte Carlo methods"> Monte Carlo methods</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20estimation" title=" statistical estimation"> statistical estimation</a> </p> <a href="https://publications.waset.org/abstracts/63592/monte-carlo-methods-and-statistical-inference-of-multitype-branching-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63592.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8680</span> Towards Integrating Statistical Color Features for Human Skin Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Zamri%20Osman">Mohd Zamri Osman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Aizaini%20Maarof"> Mohd Aizaini Maarof</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Foad%20Rohani"> Mohd Foad Rohani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20space" title="color space">color space</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20detection" title=" skin detection"> skin detection</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20feature" title=" statistical feature"> statistical feature</a> </p> <a href="https://publications.waset.org/abstracts/43485/towards-integrating-statistical-color-features-for-human-skin-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43485.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8679</span> Investigating the Effects of Data Transformations on a Bi-Dimensional Chi-Square Test</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexandru%20George%20Vaduva">Alexandru George Vaduva</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriana%20Vlad"> Adriana Vlad</a>, <a href="https://publications.waset.org/abstracts/search?q=Bogdan%20Badea"> Bogdan Badea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, we conduct a Monte Carlo analysis on a two-dimensional χ2 test, which is used to determine the minimum distance required for independent sampling in the context of chaotic signals. We investigate the impact of transforming initial data sets from any probability distribution to new signals with a uniform distribution using the Spearman rank correlation on the χ2 test. This transformation removes the randomness of the data pairs, and as a result, the observed distribution of χ2 test values differs from the expected distribution. We propose a solution to this problem and evaluate it using another chaotic signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20signals" title="chaotic signals">chaotic signals</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20map" title=" logistic map"> logistic map</a>, <a href="https://publications.waset.org/abstracts/search?q=Pearson%E2%80%99s%20test" title=" Pearson’s test"> Pearson’s test</a>, <a href="https://publications.waset.org/abstracts/search?q=Chi%20Square%20test" title=" Chi Square test"> Chi Square test</a>, <a href="https://publications.waset.org/abstracts/search?q=bivariate%20distribution" title=" bivariate distribution"> bivariate distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20independence" title=" statistical independence"> statistical independence</a> </p> <a href="https://publications.waset.org/abstracts/161938/investigating-the-effects-of-data-transformations-on-a-bi-dimensional-chi-square-test" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161938.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8678</span> Characteristic Function in Estimation of Probability Distribution Moments </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20S.%20Timofeev">Vladimir S. Timofeev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article the problem of distributional moments estimation is considered. The new approach of moments estimation based on usage of the characteristic function is proposed. By statistical simulation technique, author shows that new approach has some robust properties. For calculation of the derivatives of characteristic function there is used numerical differentiation. Obtained results confirmed that author’s idea has a certain working efficiency and it can be recommended for any statistical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=characteristic%20function" title="characteristic function">characteristic function</a>, <a href="https://publications.waset.org/abstracts/search?q=distributional%20moments" title=" distributional moments"> distributional moments</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20estimation%20problem" title=" statistical estimation problem"> statistical estimation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20simulation" title=" statistical simulation"> statistical simulation</a> </p> <a href="https://publications.waset.org/abstracts/11779/characteristic-function-in-estimation-of-probability-distribution-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11779.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8677</span> Explore Urban Spatial Density with Boltzmann Statistical Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianjia%20Wang">Jianjia Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20Yu"> Tong Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haoran%20Zhu"> Haoran Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kun%20Liu"> Kun Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwei%20Hao"> Jinwei Hao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The underlying pattern in the modern city is agglomeration. To some degree, the distribution of urban spatial density can be used to describe the status of this assemblage. There are three intrinsic characteristics to measure urban spatial density, namely, Floor Area Ratio (FAR), Building Coverage Ratio (BCR), and Average Storeys (AS). But the underlying mechanism that contributes to these quantities is still vague in the statistical urban study. In this paper, we explore the corresponding extrinsic factors related to spatial density. These factors can further provide the potential influence on the intrinsic quantities. Here, we take Shanghai Inner Ring Area and Manhattan in New York as examples to analyse the potential impacts on urban spatial density with six selected extrinsic elements. Ebery single factor presents the correlation to the spatial distribution, but the overall global impact of all is still implicit. To handle this issue, we attempt to develop the Boltzmann statistical model to explicitly explain the mechanism behind that. We derive a corresponding novel quantity, called capacity, to measure the global effects of all other extrinsic factors to the three intrinsic characteristics. The distribution of capacity presents a similar pattern to real measurements. This reveals the nonlinear influence on the multi-factor relations to the urban spatial density in agglomeration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20spatial%20density" title="urban spatial density">urban spatial density</a>, <a href="https://publications.waset.org/abstracts/search?q=Boltzmann%20statistics" title=" Boltzmann statistics"> Boltzmann statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-factor%20correlation" title=" multi-factor correlation"> multi-factor correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20distribution" title=" spatial distribution"> spatial distribution</a> </p> <a href="https://publications.waset.org/abstracts/148943/explore-urban-spatial-density-with-boltzmann-statistical-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8676</span> Application of Hyperbinomial Distribution in Developing a Modified p-Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed">Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria"> M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20distribution" title="binomial distribution">binomial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title=" cumulative distribution function"> cumulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper%20binomial%20distribution" title=" hyper binomial distribution"> hyper binomial distribution</a> </p> <a href="https://publications.waset.org/abstracts/90750/application-of-hyperbinomial-distribution-in-developing-a-modified-p-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8675</span> Analytical Slope Stability Analysis Based on the Statistical Characterization of Soil Shear Strength</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernardo%20C.%20P.%20Albuquerque">Bernardo C. P. Albuquerque</a>, <a href="https://publications.waset.org/abstracts/search?q=Darym%20J.%20F.%20Campos"> Darym J. F. Campos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20slope%20stability%20analysis" title="statistical slope stability analysis">statistical slope stability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=skew%20distributions" title=" skew distributions"> skew distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20failure" title=" probability of failure"> probability of failure</a>, <a href="https://publications.waset.org/abstracts/search?q=functions%20of%20random%20variables" title=" functions of random variables"> functions of random variables</a> </p> <a href="https://publications.waset.org/abstracts/35856/analytical-slope-stability-analysis-based-on-the-statistical-characterization-of-soil-shear-strength" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8674</span> The Effect of Excel on Undergraduate Students’ Understanding of Statistics and the Normal Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masomeh%20Jamshid%20Nejad">Masomeh Jamshid Nejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, statistical literacy is no longer a necessary skill but an essential skill with broad applications across diverse fields, especially in operational decision areas such as business management, finance, and economics. As such, learning and deep understanding of statistical concepts are essential in the context of business studies. One of the crucial topics in statistical theory and its application is the normal distribution, often called a bell-shaped curve. To interpret data and conduct hypothesis tests, comprehending the properties of normal distribution (the mean and standard deviation) is essential for business students. This requires undergraduate students in the field of economics and business management to visualize and work with data following a normal distribution. Since technology is interconnected with education these days, it is important to teach statistics topics in the context of Python, R-studio, and Microsoft Excel to undergraduate students. This research endeavours to shed light on the effect of Excel-based instruction on learners’ knowledge of statistics, specifically the central concept of normal distribution. As such, two groups of undergraduate students (from the Business Management program) were compared in this research study. One group underwent Excel-based instruction and another group relied only on traditional teaching methods. We analyzed experiential data and BBA participants’ responses to statistic-related questions focusing on the normal distribution, including its key attributes, such as the mean and standard deviation. The results of our study indicate that exposing students to Excel-based learning supports learners in comprehending statistical concepts more effectively compared with the other group of learners (teaching with the traditional method). In addition, students in the context of Excel-based instruction showed ability in picturing and interpreting data concentrated on normal distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistics" title="statistics">statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=excel-based%20instruction" title=" excel-based instruction"> excel-based instruction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title=" data visualization"> data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=pedagogy" title=" pedagogy"> pedagogy</a> </p> <a href="https://publications.waset.org/abstracts/175793/the-effect-of-excel-on-undergraduate-students-understanding-of-statistics-and-the-normal-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175793.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8673</span> Distribution-Free Exponentially Weighted Moving Average Control Charts for Monitoring Process Variability </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen-Fang%20Tsai">Chen-Fang Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin-Li%20Lu"> Shin-Li Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Distribution-free control chart is an oncoming area from the statistical process control charts in recent years. Some researchers have developed various nonparametric control charts and investigated the detection capability of these charts. The major advantage of nonparametric control charts is that the underlying process is not specifically considered the assumption of normality or any parametric distribution. In this paper, two nonparametric exponentially weighted moving average (EWMA) control charts based on nonparametric tests, namely NE-S and NE-M control charts, are proposed for monitoring process variability. Generally, weighted moving average (GWMA) control charts are extended by utilizing design and adjustment parameters for monitoring the changes in the process variability, namely NG-S and NG-M control charts. Statistical performance is also investigated on NG-S and NG-M control charts with run rules. Moreover, sensitivity analysis is performed to show the effects of design parameters under the nonparametric NG-S and NG-M control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distribution-free%20control%20chart" title="Distribution-free control chart">Distribution-free control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=EWMA%20control%20charts" title=" EWMA control charts"> EWMA control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=GWMA%20control%20charts" title=" GWMA control charts"> GWMA control charts</a> </p> <a href="https://publications.waset.org/abstracts/88638/distribution-free-exponentially-weighted-moving-average-control-charts-for-monitoring-process-variability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88638.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8672</span> Quantitative Assessment of Soft Tissues by Statistical Analysis of Ultrasound Backscattered Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Da-Ming%20Huang">Da-Ming Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya-Ting%20Tsai"> Ya-Ting Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyh-Hau%20Wang"> Shyh-Hau Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ultrasound signals backscattered from the soft tissues are mainly depending on the size, density, distribution, and other elastic properties of scatterers in the interrogated sample volume. The quantitative analysis of ultrasonic backscattering is frequently implemented using the statistical approach due to that of backscattering signals tends to be with the nature of the random variable. Thus, the statistical analysis, such as Nakagami statistics, has been applied to characterize the density and distribution of scatterers of a sample. Yet, the accuracy of statistical analysis could be readily affected by the receiving signals associated with the nature of incident ultrasound wave and acoustical properties of samples. Thus, in the present study, efforts were made to explore such effects as the ultrasound operational modes and attenuation of biological tissue on the estimation of corresponding Nakagami statistical parameter (m parameter). In vitro measurements were performed from healthy and pathological fibrosis porcine livers using different single-element ultrasound transducers and duty cycles of incident tone burst ranging respectively from 3.5 to 7.5 MHz and 10 to 50%. Results demonstrated that the estimated m parameter tends to be sensitively affected by the use of ultrasound operational modes as well as the tissue attenuation. The healthy and pathological tissues may be characterized quantitatively by m parameter under fixed measurement conditions and proper calibration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20backscattering" title="ultrasound backscattering">ultrasound backscattering</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20mode" title=" operational mode"> operational mode</a>, <a href="https://publications.waset.org/abstracts/search?q=attenuation" title=" attenuation"> attenuation</a> </p> <a href="https://publications.waset.org/abstracts/46401/quantitative-assessment-of-soft-tissues-by-statistical-analysis-of-ultrasound-backscattered-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8671</span> Analysis of the Statistical Characterization of Significant Wave Data Exceedances for Designing Offshore Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Teixeira">Rui Teixeira</a>, <a href="https://publications.waset.org/abstracts/search?q=Alan%20O%E2%80%99Connor"> Alan O’Connor</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Nogal"> Maria Nogal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20events" title="extreme events">extreme events</a>, <a href="https://publications.waset.org/abstracts/search?q=offshore%20structures" title=" offshore structures"> offshore structures</a>, <a href="https://publications.waset.org/abstracts/search?q=peak-over-threshold" title=" peak-over-threshold"> peak-over-threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=significant%20wave%20data" title=" significant wave data"> significant wave data</a> </p> <a href="https://publications.waset.org/abstracts/56287/analysis-of-the-statistical-characterization-of-significant-wave-data-exceedances-for-designing-offshore-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56287.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">273</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8670</span> Analysis of Operating Speed on Four-Lane Divided Highways under Mixed Traffic Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaitanya%20Varma">Chaitanya Varma</a>, <a href="https://publications.waset.org/abstracts/search?q=Arpan%20Mehar"> Arpan Mehar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=highway" title="highway">highway</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20traffic%20flow" title=" mixed traffic flow"> mixed traffic flow</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=operating%20speed" title=" operating speed"> operating speed</a> </p> <a href="https://publications.waset.org/abstracts/33813/analysis-of-operating-speed-on-four-lane-divided-highways-under-mixed-traffic-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">460</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8669</span> A Brief Study about Nonparametric Adherence Tests</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinicius%20R.%20Domingues">Vinicius R. Domingues</a>, <a href="https://publications.waset.org/abstracts/search?q=Luan%20C.%20S.%20M.%20Ozelim"> Luan C. S. M. Ozelim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kolmogorov-Smirnov%20test" title="Kolmogorov-Smirnov test">Kolmogorov-Smirnov test</a>, <a href="https://publications.waset.org/abstracts/search?q=Anderson-Darling%20test" title=" Anderson-Darling test"> Anderson-Darling test</a>, <a href="https://publications.waset.org/abstracts/search?q=Cramer-Von-Mises%20test" title=" Cramer-Von-Mises test"> Cramer-Von-Mises test</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20adherence%20tests" title=" nonparametric adherence tests"> nonparametric adherence tests</a> </p> <a href="https://publications.waset.org/abstracts/35858/a-brief-study-about-nonparametric-adherence-tests" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35858.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8668</span> Research on Transmission Parameters Determination Method Based on Dynamic Characteristic Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baoshan%20Huang">Baoshan Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fanbiao%20Bao"> Fanbiao Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Bing%20Li"> Bing Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Lianghua%20Zeng"> Lianghua Zeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Zheng"> Yi Zheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parameter control strategy based on statistical characteristics can analyze the choice of the transmission ratio of an automobile transmission. According to the difference of the transmission gear, the number and spacing of the gear can be determined. Transmission ratio distribution of transmission needs to satisfy certain distribution law. According to the statistic characteristics of driving parameters, the shift control strategy of the vehicle is analyzed. CVT shift schedule adjustment algorithm based on statistical characteristic parameters can be seen from the above analysis, if according to the certain algorithm to adjust the size of, can adjust the target point are in the best efficiency curve and dynamic curve between the location, to alter the vehicle characteristics. Based on the dynamic characteristics and the practical application of the vehicle, this paper presents the setting scheme of the transmission ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20dynamics" title="vehicle dynamics">vehicle dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=transmission%20ratio" title=" transmission ratio"> transmission ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=transmission%20parameters" title=" transmission parameters"> transmission parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20characteristics" title=" statistical characteristics"> statistical characteristics</a> </p> <a href="https://publications.waset.org/abstracts/53642/research-on-transmission-parameters-determination-method-based-on-dynamic-characteristic-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53642.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8667</span> First Order Reversal Curve Method for Characterization of Magnetic Nanostructures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bashara%20Want">Bashara Want</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the key factors limiting the performance of magnetic memory is that the coercivity has a distribution with finite width, and the reversal starts at the weakest link in the distribution. So one must first know the distribution of coercivities in order to learn how to reduce the width of distribution and increase the coercivity field to obtain a system with narrow width. First Order Reversal Curve (FORC) method characterizes a system with hysteresis via the distribution of local coercivities and, in addition, the local interaction field. The method is more versatile than usual conventional major hysteresis loops that give only the statistical behaviour of the magnetic system. The FORC method will be presented and discussed at the conference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20materials" title="magnetic materials">magnetic materials</a>, <a href="https://publications.waset.org/abstracts/search?q=hysteresis" title=" hysteresis"> hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=first-order%20reversal%20curve%20method" title=" first-order reversal curve method"> first-order reversal curve method</a>, <a href="https://publications.waset.org/abstracts/search?q=nanostructures" title=" nanostructures"> nanostructures</a> </p> <a href="https://publications.waset.org/abstracts/155553/first-order-reversal-curve-method-for-characterization-of-magnetic-nanostructures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8666</span> Base Change for Fisher Metrics: Case of the q-Gaussian Inverse Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20I.%20Loaiza%20Ossa">Gabriel I. Loaiza Ossa</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20Cadavid%20Moreno"> Carlos A. Cadavid Moreno</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20C.%20%20Arango%20Parra"> Juan C. Arango Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that the Riemannian manifold determined by the family of inverse Gaussian distributions endowed with the Fisher metric has negative constant curvature κ= -1/2, as does the family of usual Gaussian distributions. In the present paper, firstly, we arrive at this result by following a different path, much simpler than the previous ones. We first put the family in exponential form, thus endowing the family with a new set of parameters, or coordinates, θ₁, θ₂; then we determine the matrix of the Fisher metric in terms of these parameters; and finally we compute this matrix in the original parameters. Secondly, we define the inverse q-Gaussian distribution family (q < 3) as the family obtained by replacing the usual exponential function with the Tsallis q-exponential function in the expression for the inverse Gaussian distribution and observe that it supports two possible geometries, the Fisher and the q-Fisher geometry. And finally, we apply our strategy to obtain results about the Fisher and q-Fisher geometry of the inverse q-Gaussian distribution family, similar to the ones obtained in the case of the inverse Gaussian distribution family. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=base%20of%20changes" title="base of changes">base of changes</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Gaussian%20distribution" title=" inverse Gaussian distribution"> inverse Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20q-Gaussian%20distribution" title=" inverse q-Gaussian distribution"> inverse q-Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20manifolds" title=" statistical manifolds"> statistical manifolds</a> </p> <a href="https://publications.waset.org/abstracts/138122/base-change-for-fisher-metrics-case-of-the-q-gaussian-inverse-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8665</span> Classical and Bayesian Inference of the Generalized Log-Logistic Distribution with Applications to Survival Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdisalam%20Hassan%20Muse">Abdisalam Hassan Muse</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Mwalili"> Samuel Mwalili</a>, <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Ngesa"> Oscar Ngesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized log-logistic distribution with variable shapes of the hazard rate was introduced and studied, extending the log-logistic distribution by adding an extra parameter to the classical distribution, leading to greater flexibility in analysing and modeling various data types. The proposed distribution has a large number of well-known lifetime special sub-models such as; Weibull, log-logistic, exponential, and Burr XII distributions. Its basic mathematical and statistical properties were derived. The method of maximum likelihood was adopted for estimating the unknown parameters of the proposed distribution, and a Monte Carlo simulation study is carried out to assess the behavior of the estimators. The importance of this distribution is that its tendency to model both monotone (increasing and decreasing) and non-monotone (unimodal and bathtub shape) or reversed “bathtub” shape hazard rate functions which are quite common in survival and reliability data analysis. Furthermore, the flexibility and usefulness of the proposed distribution are illustrated in a real-life data set and compared to its sub-models; Weibull, log-logistic, and BurrXII distributions and other parametric survival distributions with 3-parmaeters; like the exponentiated Weibull distribution, the 3-parameter lognormal distribution, the 3- parameter gamma distribution, the 3-parameter Weibull distribution, and the 3-parameter log-logistic (also known as shifted log-logistic) distribution. The proposed distribution provided a better fit than all of the competitive distributions based on the goodness-of-fit tests, the log-likelihood, and information criterion values. Finally, Bayesian analysis and performance of Gibbs sampling for the data set are also carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hazard%20rate%20function" title="hazard rate function">hazard rate function</a>, <a href="https://publications.waset.org/abstracts/search?q=log-logistic%20distribution" title=" log-logistic distribution"> log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20log-logistic%20distribution" title=" generalized log-logistic distribution"> generalized log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20data" title=" survival data"> survival data</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/139326/classical-and-bayesian-inference-of-the-generalized-log-logistic-distribution-with-applications-to-survival-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8664</span> A Nonlocal Means Algorithm for Poisson Denoising Based on Information Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongxu%20Chen">Dongxu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yipeng%20Li"> Yipeng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an information geometry NonlocalMeans(NLM) algorithm for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches in Euclidean space. In this work, every pixel is a Poisson distribution locally estimated by Maximum Likelihood (ML), all distributions consist of a statistical manifold. A NLM denoising algorithm is conducted on the statistical manifold where Fisher information matrix can be used for computing distribution geodesics referenced as the similarity between patches. This approach was demonstrated to be competitive with related state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20noise" title=" Poisson noise"> Poisson noise</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal-means" title=" nonlocal-means"> nonlocal-means</a> </p> <a href="https://publications.waset.org/abstracts/51221/a-nonlocal-means-algorithm-for-poisson-denoising-based-on-information-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8663</span> Characteristics of Cumulative Distribution Function of Grown Crack Size at Specified Fatigue Crack Propagation Life under Different Maximum Fatigue Loads in AZ31</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seon%20Soon%20Choi">Seon Soon Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnesium alloy has been widely used in structure such as an automobile. It is necessary to consider probabilistic characteristics of a structural material because a fatigue behavior of a structure has a randomness and uncertainty. The purpose of this study is to find the characteristics of the cumulative distribution function (CDF) of the grown crack size at a specified fatigue crack propagation life and to investigate a statistical crack propagation in magnesium alloys. The statistical fatigue data of the grown crack size are obtained through the fatigue crack propagation (FCP) tests under different maximum fatigue load conditions conducted on the replicated specimens of magnesium alloys. The 3-parameter Weibull distribution is used to find the CDF of grown crack size. The CDF of grown crack size in case of larger maximum fatigue load has longer tail in below 10 percent and above 90 percent. The fatigue failure occurs easily as the tail of CDF of grown crack size becomes long. The fatigue behavior under the larger maximum fatigue load condition shows more rapid propagation and failure mode. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title="cumulative distribution function">cumulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=fatigue%20crack%20propagation" title=" fatigue crack propagation"> fatigue crack propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=grown%20crack%20size" title=" grown crack size"> grown crack size</a>, <a href="https://publications.waset.org/abstracts/search?q=magnesium%20alloys" title=" magnesium alloys"> magnesium alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20fatigue%20load" title=" maximum fatigue load"> maximum fatigue load</a> </p> <a href="https://publications.waset.org/abstracts/76512/characteristics-of-cumulative-distribution-function-of-grown-crack-size-at-specified-fatigue-crack-propagation-life-under-different-maximum-fatigue-loads-in-az31" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8662</span> Lambda-Levelwise Statistical Convergence of a Sequence of Fuzzy Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Berna%20Benli">F. Berna Benli</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zg%C3%BCr%20Keskin"> Özgür Keskin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lately, many mathematicians have been studied the statistical convergence of a sequence of fuzzy numbers. We know that Lambda-statistically convergence is a kind of convergence between ordinary convergence and statistical convergence. In this paper, we will introduce the new kind of convergence such as λ-levelwise statistical convergence. Then, we will define the concept of the λ-levelwise statistical cluster and limit points of a sequence of fuzzy numbers. Also, we will discuss the relations between the sets of λ-levelwise statistical cluster points and λ-levelwise statistical limit points of sequences of fuzzy numbers. This work has been extended in this paper, where some relations have been considered such that when lambda-statistical limit inferior and lambda-statistical limit superior for lambda-statistically convergent sequences of fuzzy numbers are equal. Furthermore, lambda-statistical boundedness condition for different sequences of fuzzy numbers has been studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20number" title="fuzzy number">fuzzy number</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20cluster%20points" title=" λ-levelwise statistical cluster points"> λ-levelwise statistical cluster points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20convergence" title=" λ-levelwise statistical convergence"> λ-levelwise statistical convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20limit%20points" title=" λ-levelwise statistical limit points"> λ-levelwise statistical limit points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20cluster%20points" title=" λ-statistical cluster points"> λ-statistical cluster points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20convergence" title=" λ-statistical convergence"> λ-statistical convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20limit%20%20points" title=" λ-statistical limit points"> λ-statistical limit points</a> </p> <a href="https://publications.waset.org/abstracts/20755/lambda-levelwise-statistical-convergence-of-a-sequence-of-fuzzy-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20755.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a 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