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multilevel logit models</title> <meta name="description" content="Search results for: Bayesian multilevel logit models"> <meta name="keywords" content="Bayesian multilevel logit models"> <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" /> 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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="Bayesian multilevel logit models"> <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> 7142</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Bayesian multilevel logit models</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7142</span> Understanding Mathematics Achievements among U. S. Middle School Students: A Bayesian Multilevel Modeling Analysis with Informative Priors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Yuan">Jing Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongwei%20Yang"> Hongwei Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to understand U.S. middle school students’ mathematics achievements by examining relevant student and school-level predictors. Through a variance component analysis, the study first identifies evidence supporting the use of multilevel modeling. Then, a multilevel analysis is performed under Bayesian statistical inference where prior information is incorporated into the modeling process. During the analysis, independent variables are entered sequentially in the order of theoretical importance to create a hierarchy of models. By evaluating each model using Bayesian fit indices, a best-fit and most parsimonious model is selected where Bayesian statistical inference is performed for the purpose of result interpretation and discussion. The primary dataset for Bayesian modeling is derived from the Program for International Student Assessment (PISA) in 2012 with a secondary PISA dataset from 2003 analyzed under the traditional ordinary least squares method to provide the information needed to specify informative priors for a subset of the model parameters. The dependent variable is a composite measure of mathematics literacy, calculated from an exploratory factor analysis of all five PISA 2012 mathematics achievement plausible values for which multiple evidences are found supporting data unidimensionality. The independent variables include demographics variables and content-specific variables: mathematics efficacy, teacher-student ratio, proportion of girls in the school, etc. Finally, the entire analysis is performed using the MCMCpack and MCMCglmm packages in R. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20multilevel%20modeling" title="Bayesian multilevel modeling">Bayesian multilevel modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics%20education" title=" mathematics education"> mathematics education</a>, <a href="https://publications.waset.org/abstracts/search?q=PISA" title=" PISA"> PISA</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel" title=" multilevel"> multilevel</a> </p> <a href="https://publications.waset.org/abstracts/67623/understanding-mathematics-achievements-among-u-s-middle-school-students-a-bayesian-multilevel-modeling-analysis-with-informative-priors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67623.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">336</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">7141</span> How Polarization and Ideological Divisiveness Increase the Likelihood of Executive Action: Evidence from the Italian Case</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umberto%20Platini">Umberto Platini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyses the role of government fragmentation as predictor of the use of emergency decrees in parliamentary democracies. In particular, it focuses on the relationship between ideological divisiveness within cabinets and the choice by executives to issue emergency decrees rather initiating ordinary legislative procedures. A Bayesian multilevel analysis conducted on the population of government-initiated legislation in Italy between 1996 and 2018 finds significant evidence that those legislative proposals which are further away from the ideological centre of gravity of the executive are around three times more likely to be issued as emergency decrees. Likewise, legislative projects regulating more contentious policy areas are significantly more likely to be issued by decree. However, for more contentious issues the importance of ideological distance as a predictor diminishes. This evidence suggests that cabinets prefer decrees to ordinary legislative procedures when they expect that the bargaining environment in Parliament is more hostile. These results persist regardless of the fluctuations of the political-economic cycle. Their robustness is also tested against a battery of controls and against fixed effects both at the government level and at the legislature level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20multilevel%20logit%20models" title="Bayesian multilevel logit models">Bayesian multilevel logit models</a>, <a href="https://publications.waset.org/abstracts/search?q=executive%20action" title=" executive action"> executive action</a>, <a href="https://publications.waset.org/abstracts/search?q=executive%20decrees" title=" executive decrees"> executive decrees</a>, <a href="https://publications.waset.org/abstracts/search?q=ideology" title=" ideology"> ideology</a>, <a href="https://publications.waset.org/abstracts/search?q=legislative%20studies" title=" legislative studies"> legislative studies</a>, <a href="https://publications.waset.org/abstracts/search?q=polarization" title=" polarization"> polarization</a> </p> <a href="https://publications.waset.org/abstracts/148982/how-polarization-and-ideological-divisiveness-increase-the-likelihood-of-executive-action-evidence-from-the-italian-case" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148982.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">105</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">7140</span> The Effect of Institutions on Economic Growth: An Analysis Based on Bayesian Panel Data Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Anwar">Mohammad Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shah%20Waliullah"> Shah Waliullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title="Bayesian approach">Bayesian approach</a>, <a href="https://publications.waset.org/abstracts/search?q=common%20effect" title=" common effect"> common effect</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20effect" title=" fixed effect"> fixed effect</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20effect" title=" random effect"> random effect</a>, <a href="https://publications.waset.org/abstracts/search?q=Dynamic%20Random%20Effect%20Model" title=" Dynamic Random Effect Model"> Dynamic Random Effect Model</a> </p> <a href="https://publications.waset.org/abstracts/161692/the-effect-of-institutions-on-economic-growth-an-analysis-based-on-bayesian-panel-data-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161692.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">68</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">7139</span> Assessing Effects of an Intervention on Bottle-Weaning and Reducing Daily Milk Intake from Bottles in Toddlers Using Two-Part Random Effects Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yungtai%20Lo">Yungtai Lo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-log-normal random effects model, a two-part logit-truncated normal random effects model, a two-part logit-gamma random effects model, and a two-part logit-skew normal random effects model were used to examine effects of a bottle-weaning intervention on reducing bottle use and daily milk intake from bottles in toddlers aged 11 to 13 months in a randomized controlled trial. We show in all four two-part models that the intervention promoted bottle-weaning and reduced daily milk intake from bottles in toddlers drinking from a bottle. We also show that there are no differences in model fit using either the logit link function or the probit link function for modeling the probability of bottle-weaning in all four models. Furthermore, prediction accuracy of the logit or probit link function is not sensitive to the distribution assumption on daily milk intake from bottles in toddlers not off bottles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-part%20model" title="two-part model">two-part model</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-continuous%20variable" title=" semi-continuous variable"> semi-continuous variable</a>, <a href="https://publications.waset.org/abstracts/search?q=truncated%20normal" title=" truncated normal"> truncated normal</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20regression" title=" gamma regression"> gamma regression</a>, <a href="https://publications.waset.org/abstracts/search?q=skew%20normal" title=" skew normal"> skew normal</a>, <a href="https://publications.waset.org/abstracts/search?q=Pearson%20residual" title=" Pearson residual"> Pearson residual</a>, <a href="https://publications.waset.org/abstracts/search?q=receiver%20operating%20characteristic%20curve" title=" receiver operating characteristic curve"> receiver operating characteristic curve</a> </p> <a href="https://publications.waset.org/abstracts/46920/assessing-effects-of-an-intervention-on-bottle-weaning-and-reducing-daily-milk-intake-from-bottles-in-toddlers-using-two-part-random-effects-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46920.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">349</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">7138</span> Stock Market Developments, Income Inequality, Wealth Inequality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quang%20Dong%20Dang">Quang Dong Dang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the possible effects of stock market developments by channels on income and wealth inequality. We use the Bayesian Multilevel Model with the explanatory variables of the market’s channels, such as accessibility, efficiency, and market health in six selected countries: the US, UK, Japan, Vietnam, Thailand, and Malaysia. We found that generally, the improvements in the stock market alleviate income inequality. However, stock market expansions in higher-income countries are likely to trigger income inequality. We also found that while enhancing the quality of channels of the stock market has counter-effects on wealth equality distributions, open accessibilities help reduce wealth inequality distributions within the scope of the study. In addition, the inverted U-shaped hypothesis seems not to be valid in six selected countries between the period from 2006 to 2020. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20multilevel%20model" title="Bayesian multilevel model">Bayesian multilevel model</a>, <a href="https://publications.waset.org/abstracts/search?q=income%20inequality" title=" income inequality"> income inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=inverted%20u-shaped%20hypothesis" title=" inverted u-shaped hypothesis"> inverted u-shaped hypothesis</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20development" title=" stock market development"> stock market development</a>, <a href="https://publications.waset.org/abstracts/search?q=wealth%20inequality" title=" wealth inequality"> wealth inequality</a> </p> <a href="https://publications.waset.org/abstracts/160922/stock-market-developments-income-inequality-wealth-inequality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160922.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">108</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">7137</span> A Framework for Auditing Multilevel Models Using Explainability Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debarati%20Bhaumik">Debarati Bhaumik</a>, <a href="https://publications.waset.org/abstracts/search?q=Diptish%20Dey"> Diptish Dey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=audit" title="audit">audit</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20model" title=" multilevel model"> multilevel model</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20transparency" title=" model transparency"> model transparency</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20explainability" title=" model explainability"> model explainability</a>, <a href="https://publications.waset.org/abstracts/search?q=discrimination" title=" discrimination"> discrimination</a>, <a href="https://publications.waset.org/abstracts/search?q=ethics" title=" ethics"> ethics</a> </p> <a href="https://publications.waset.org/abstracts/150654/a-framework-for-auditing-multilevel-models-using-explainability-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150654.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">94</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">7136</span> Optimized Dynamic Bayesian Networks and Neural Verifier Test Applied to On-Line Isolated Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Redouane"> Redouane</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Kouninef"> Belkacem Kouninef</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks "> networks </a> </p> <a href="https://publications.waset.org/abstracts/34593/optimized-dynamic-bayesian-networks-and-neural-verifier-test-applied-to-on-line-isolated-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34593.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">617</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">7135</span> Factorization of Computations in Bayesian Networks: Interpretation of Factors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Smail">Linda Smail</a>, <a href="https://publications.waset.org/abstracts/search?q=Zineb%20Azouz"> Zineb Azouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title="Bayesian networks">Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=D-Separation" title=" D-Separation"> D-Separation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20two%20Bayesian%20networks" title=" level two Bayesian networks"> level two Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=factorization%20of%20computation" title=" factorization of computation"> factorization of computation</a> </p> <a href="https://publications.waset.org/abstracts/18829/factorization-of-computations-in-bayesian-networks-interpretation-of-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18829.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">529</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">7134</span> Comparison of Methods of Estimation for Use in Goodness of Fit Tests for Binary Multilevel Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20V.%20Pinto">I. V. Pinto</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Sooriyarachchi"> M. R. Sooriyarachchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20test" title="goodness-of-fit test">goodness-of-fit test</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20quasi-likelihood" title=" marginal quasi-likelihood"> marginal quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20modelling" title=" multilevel modelling"> multilevel modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20quasi-likelihood" title=" penalized quasi-likelihood"> penalized quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=power" title=" power"> power</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-likelihood" title=" quasi-likelihood"> quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20error" title=" type-I error"> type-I error</a> </p> <a href="https://publications.waset.org/abstracts/105519/comparison-of-methods-of-estimation-for-use-in-goodness-of-fit-tests-for-binary-multilevel-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105519.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">142</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">7133</span> Multilevel Modeling of the Progression of HIV/AIDS Disease among Patients under HAART Treatment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Awol%20Seid%20Ebrie">Awol Seid Ebrie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonproportional%20odds%20model" title="nonproportional odds model">nonproportional odds model</a>, <a href="https://publications.waset.org/abstracts/search?q=proportional%20odds%20model" title=" proportional odds model"> proportional odds model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20coefficients%20model" title=" random coefficients model"> random coefficients model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20intercepts%20model" title=" random intercepts model"> random intercepts model</a> </p> <a href="https://publications.waset.org/abstracts/34642/multilevel-modeling-of-the-progression-of-hivaids-disease-among-patients-under-haart-treatment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34642.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">7132</span> New Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise" title=" piecewise"> piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20Jump%20MCMC" title=" reversible Jump MCMC"> reversible Jump MCMC</a> </p> <a href="https://publications.waset.org/abstracts/31651/new-segmentation-of-piecewise-linear-regression-models-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31651.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">521</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">7131</span> Bayesian Meta-Analysis to Account for Heterogeneity in Studies Relating Life Events to Disease </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Stojanovski">Elizabeth Stojanovski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Associations between life events and various forms of cancers have been identified. The purpose of a recent random-effects meta-analysis was to identify studies that examined the association between adverse events associated with changes to financial status including decreased income and breast cancer risk. The same association was studied in four separate studies which displayed traits that were not consistent between studies such as the study design, location and time frame. It was of interest to pool information from various studies to help identify characteristics that differentiated study results. Two random-effects Bayesian meta-analysis models are proposed to combine the reported estimates of the described studies. The proposed models allow major sources of variation to be taken into account, including study level characteristics, between study variance, and within study variance and illustrate the ease with which uncertainty can be incorporated using a hierarchical Bayesian modelling approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=random-effects" title="random-effects">random-effects</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-analysis" title=" meta-analysis"> meta-analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=variation" title=" variation"> variation</a> </p> <a href="https://publications.waset.org/abstracts/100263/bayesian-meta-analysis-to-account-for-heterogeneity-in-studies-relating-life-events-to-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100263.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">160</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">7130</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.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">395</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">7129</span> Multidisciplinary and Multilevel Design Methodology of Unmanned Aerial Vehicles using Enhanced Collaborative Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pedro%20F.%20Albuquerque">Pedro F. Albuquerque</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20V.%20Gamboa"> Pedro V. Gamboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20A.%20Silvestre"> Miguel A. Silvestre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present work describes the implementation of the Enhanced Collaborative Optimization (ECO) multilevel architecture with a gradient-based optimization algorithm with the aim of performing a multidisciplinary design optimization of a generic unmanned aerial vehicle with morphing technologies. The concepts of weighting coefficient and a dynamic compatibility parameter are presented for the ECO architecture. A routine that calculates the aircraft performance for the user defined mission profile and vehicle’s performance requirements has been implemented using low fidelity models for the aerodynamics, stability, propulsion, weight, balance and flight performance. A benchmarking case study for evaluating the advantage of using a variable span wing within the optimization methodology developed is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidisciplinary" title="multidisciplinary">multidisciplinary</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel" title=" multilevel"> multilevel</a>, <a href="https://publications.waset.org/abstracts/search?q=morphing" title=" morphing"> morphing</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20collaborative%20optimization" title=" enhanced collaborative optimization"> enhanced collaborative optimization</a> </p> <a href="https://publications.waset.org/abstracts/18259/multidisciplinary-and-multilevel-design-methodology-of-unmanned-aerial-vehicles-using-enhanced-collaborative-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18259.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">929</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">7128</span> Bayesian Flexibility Modelling of the Conditional Autoregressive Prior in a Disease Mapping Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davies%20Obaromi">Davies Obaromi</a>, <a href="https://publications.waset.org/abstracts/search?q=Qin%20Yongsong"> Qin Yongsong</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Ndege"> James Ndege</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeez%20Adeboye"> Azeez Adeboye</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinwumi%20Odeyemi"> Akinwumi Odeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Besag2" title="Besag2">Besag2</a>, <a href="https://publications.waset.org/abstracts/search?q=CAR%20models" title=" CAR models"> CAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20mapping" title=" disease mapping"> disease mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20models" title=" spatial models"> spatial models</a> </p> <a href="https://publications.waset.org/abstracts/77683/bayesian-flexibility-modelling-of-the-conditional-autoregressive-prior-in-a-disease-mapping-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77683.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">7127</span> Spatial Econometric Approaches for Count Data: An Overview and New Directions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paula%20Sim%C3%B5es">Paula Simões</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabel%20Nat%C3%A1rio"> Isabel Natário</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis" title="spatial data analysis">spatial data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20econometrics" title=" spatial econometrics"> spatial econometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20hierarchical%20models" title=" Bayesian hierarchical models"> Bayesian hierarchical models</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a> </p> <a href="https://publications.waset.org/abstracts/35788/spatial-econometric-approaches-for-count-data-an-overview-and-new-directions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35788.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">593</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">7126</span> Hybrid PWM Techniques for the Reduction of Switching Losses and Voltage Harmonics in Cascaded Multilevel Inverters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Venkata%20Reddy%20Kota">Venkata Reddy Kota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> These days, the industrial trend is moving away from heavy and bulky passive components to power converter systems that use more and more semiconductor elements. Also, it is difficult to connect the traditional converters to the high and medium voltage. For these reasons, a new family of multilevel inverters has appeared as a solution for working with higher voltage levels. Different modulation topologies like Sinusoidal Pulse Width Modulation (SPWM), Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) are available for multilevel inverters. In this work, different hybrid modulation techniques which are combination of fundamental frequency modulation and multilevel sinusoidal-modulation are compared. The main characteristic of these modulations are reduction of switching losses with good harmonic performance and balanced power loss dissipation among the device. The proposed hybrid modulation schemes are developed and simulated in Matlab/Simulink for cascaded H-bridge inverter. The results validate the applicability of the proposed schemes for cascaded multilevel inverter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20PWM%20techniques" title="hybrid PWM techniques">hybrid PWM techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=cascaded%20multilevel%20inverters" title=" cascaded multilevel inverters"> cascaded multilevel inverters</a>, <a href="https://publications.waset.org/abstracts/search?q=switching%20loss%20minimization" title=" switching loss minimization"> switching loss minimization</a> </p> <a href="https://publications.waset.org/abstracts/12634/hybrid-pwm-techniques-for-the-reduction-of-switching-losses-and-voltage-harmonics-in-cascaded-multilevel-inverters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12634.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">616</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">7125</span> Constructing a Bayesian Network for Solar Energy in Egypt Using Life Cycle Analysis and Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rawaa%20H.%20El-Bidweihy">Rawaa H. El-Bidweihy</a>, <a href="https://publications.waset.org/abstracts/search?q=Hisham%20M.%20Abdelsalam"> Hisham M. Abdelsalam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihab%20A.%20El-Khodary"> Ihab A. El-Khodary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20correlation" title=" auto correlation"> auto correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20models" title=" forecasting models"> forecasting models</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20cycle" title=" life cycle"> life cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20correlation" title=" partial correlation"> partial correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=SARIMA" title=" SARIMA"> SARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20energy" title=" solar energy"> solar energy</a> </p> <a href="https://publications.waset.org/abstracts/127931/constructing-a-bayesian-network-for-solar-energy-in-egypt-using-life-cycle-analysis-and-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127931.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">155</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">7124</span> Forecasting Stock Indexes Using Bayesian Additive Regression Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darren%20Zou">Darren Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BART" title="BART">BART</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=stock" title=" stock"> stock</a> </p> <a href="https://publications.waset.org/abstracts/124504/forecasting-stock-indexes-using-bayesian-additive-regression-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124504.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">130</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">7123</span> Discrete Choice Modeling in Education: Evaluating Early Childhood Educators’ Practices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michalis%20Linardakis">Michalis Linardakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Grammatikopoulos"> Vasilis Grammatikopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Athanasios%20Gregoriadis"> Athanasios Gregoriadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalliopi%20Trouli"> Kalliopi Trouli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Discrete choice models belong to the family of Conjoint analysis that are applied on the preferences of the respondents towards a set of scenarios that describe alternative choices. The scenarios have been pre-designed to cover all the attributes of the alternatives that may affect the choices. In this study, we examine how preschool educators integrate physical activities into their everyday teaching practices through the use of discrete choice models. One of the advantages of discrete choice models compared to other more traditional data collection methods (e.g. questionnaires and interviews that use ratings) is that the respondent is called to select among competitive and realistic alternatives, rather than objectively rate each attribute that the alternatives may have. We present the effort to construct and choose representative attributes that would cover all possible choices of the respondents, and the scenarios that have arisen. For the purposes of the study, we used a sample of 50 preschool educators in Greece that responded to 4 scenarios (from the total of 16 scenarios that the orthogonal design resulted), with each scenario having three alternative teaching practices. Seven attributes of the alternatives were used in the scenarios. For the analysis of the data, we used multinomial logit model with random effects, multinomial probit model and generalized mixed logit model. The conclusions drawn from the estimated parameters of the models are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjoint%20analysis" title="conjoint analysis">conjoint analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20choice%20models" title=" discrete choice models"> discrete choice models</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data" title=" educational data"> educational data</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20statistical%20analysis" title=" multivariate statistical analysis"> multivariate statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/27531/discrete-choice-modeling-in-education-evaluating-early-childhood-educators-practices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27531.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">465</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">7122</span> Early Warning System of Financial Distress Based On Credit Cycle Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bi-Huei%20Tsai">Bi-Huei Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Multinomial%20logit%20model" title="Multinomial logit model">Multinomial logit model</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20governance" title=" corporate governance"> corporate governance</a>, <a href="https://publications.waset.org/abstracts/search?q=company%20failure" title=" company failure"> company failure</a>, <a href="https://publications.waset.org/abstracts/search?q=reorganization" title=" reorganization"> reorganization</a>, <a href="https://publications.waset.org/abstracts/search?q=bankruptcy" title=" bankruptcy"> bankruptcy</a> </p> <a href="https://publications.waset.org/abstracts/23937/early-warning-system-of-financial-distress-based-on-credit-cycle-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23937.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">377</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">7121</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.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">319</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">7120</span> A Flexible Bayesian State-Space Modelling for Population Dynamics of Wildlife and Livestock Populations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabyasachi%20Mukhopadhyay">Sabyasachi Mukhopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Ogutu"> Joseph Ogutu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hans-Peter%20Piepho"> Hans-Peter Piepho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20state-space%20model" title="bayesian state-space model">bayesian state-space model</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chain%20Monte%20Carlo" title=" Markov Chain Monte Carlo"> Markov Chain Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=population%20dynamics" title=" population dynamics"> population dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=conservation" title=" conservation"> conservation</a> </p> <a href="https://publications.waset.org/abstracts/93814/a-flexible-bayesian-state-space-modelling-for-population-dynamics-of-wildlife-and-livestock-populations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93814.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">208</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">7119</span> Empirical Roughness Progression Models of Heavy Duty Rural Pavements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nahla%20H.%20Alaswadko">Nahla H. Alaswadko</a>, <a href="https://publications.waset.org/abstracts/search?q=Rayya%20A.%20Hassan"> Rayya A. Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayar%20N.%20Mohammed"> Bayar N. Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=roughness%20progression" title="roughness progression">roughness progression</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20model" title=" empirical model"> empirical model</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20performance" title=" pavement performance"> pavement performance</a>, <a href="https://publications.waset.org/abstracts/search?q=heavy%20duty%20pavement" title=" heavy duty pavement"> heavy duty pavement</a> </p> <a href="https://publications.waset.org/abstracts/81268/empirical-roughness-progression-models-of-heavy-duty-rural-pavements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81268.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">168</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">7118</span> Application of Principal Component Analysis and Ordered Logit Model in Diabetic Kidney Disease Progression in People with Type 2 Diabetes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mequanent%20Wale%20Mekonen">Mequanent Wale Mekonen</a>, <a href="https://publications.waset.org/abstracts/search?q=Edoardo%20Otranto"> Edoardo Otranto</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Alibrandi"> Angela Alibrandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetic kidney disease is one of the main microvascular complications caused by diabetes. Several clinical and biochemical variables are reported to be associated with diabetic kidney disease in people with type 2 diabetes. However, their interrelations could distort the effect estimation of these variables for the disease's progression. The objective of the study is to determine how the biochemical and clinical variables in people with type 2 diabetes are interrelated with each other and their effects on kidney disease progression through advanced statistical methods. First, principal component analysis was used to explore how the biochemical and clinical variables intercorrelate with each other, which helped us reduce a set of correlated biochemical variables to a smaller number of uncorrelated variables. Then, ordered logit regression models (cumulative, stage, and adjacent) were employed to assess the effect of biochemical and clinical variables on the order-level response variable (progression of kidney function) by considering the proportionality assumption for more robust effect estimation. This retrospective cross-sectional study retrieved data from a type 2 diabetic cohort in a polyclinic hospital at the University of Messina, Italy. The principal component analysis yielded three uncorrelated components. These are principal component 1, with negative loading of glycosylated haemoglobin, glycemia, and creatinine; principal component 2, with negative loading of total cholesterol and low-density lipoprotein; and principal component 3, with negative loading of high-density lipoprotein and a positive load of triglycerides. The ordered logit models (cumulative, stage, and adjacent) showed that the first component (glycosylated haemoglobin, glycemia, and creatinine) had a significant effect on the progression of kidney disease. For instance, the cumulative odds model indicated that the first principal component (linear combination of glycosylated haemoglobin, glycemia, and creatinine) had a strong and significant effect on the progression of kidney disease, with an effect or odds ratio of 0.423 (P value = 0.000). However, this effect was inconsistent across levels of kidney disease because the first principal component did not meet the proportionality assumption. To address the proportionality problem and provide robust effect estimates, alternative ordered logit models, such as the partial cumulative odds model, the partial adjacent category model, and the partial continuation ratio model, were used. These models suggested that clinical variables such as age, sex, body mass index, medication (metformin), and biochemical variables such as glycosylated haemoglobin, glycemia, and creatinine have a significant effect on the progression of kidney disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20kidney%20disease" title="diabetic kidney disease">diabetic kidney disease</a>, <a href="https://publications.waset.org/abstracts/search?q=ordered%20logit%20model" title=" ordered logit model"> ordered logit model</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=type%202%20diabetes" title=" type 2 diabetes"> type 2 diabetes</a> </p> <a href="https://publications.waset.org/abstracts/186851/application-of-principal-component-analysis-and-ordered-logit-model-in-diabetic-kidney-disease-progression-in-people-with-type-2-diabetes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186851.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">39</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">7117</span> A Multilevel Approach for Stroke Prediction Combining Risk Factors and Retinal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeena%20R.%20S.">Jeena R. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sukesh%20Kumar%20A."> Sukesh Kumar A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stroke is one of the major reasons of adult disability and morbidity in many of the developing countries like India. Early diagnosis of stroke is essential for timely prevention and cure. Various conventional statistical methods and computational intelligent models have been developed for predicting the risk and outcome of stroke. This research work focuses on a multilevel approach for predicting the occurrence of stroke based on various risk factors and invasive techniques like retinal imaging. This risk prediction model can aid in clinical decision making and help patients to have an improved and reliable risk prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20imaging" title=" retinal imaging"> retinal imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factors" title=" risk factors"> risk factors</a>, <a href="https://publications.waset.org/abstracts/search?q=stroke" title=" stroke"> stroke</a> </p> <a href="https://publications.waset.org/abstracts/91133/a-multilevel-approach-for-stroke-prediction-combining-risk-factors-and-retinal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91133.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">303</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">7116</span> Implementation of a Novel Modified Multilevel Inverter Topology for Grid Connected PV System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dhivya%20Balakrishnan">Dhivya Balakrishnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhamodharan%20Shanmugam"> Dhamodharan Shanmugam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multilevel converters offer high power capability, associated with lower output harmonics and lower commutation losses. Their main disadvantage is their complexity requiring a great number of power devices and passive components, and a rather complex control circuitry. This paper proposes a single-phase seven-level inverter for grid connected PV systems, With a novel pulse width-modulated (PWM) control scheme. Three reference signals that are identical to each other with an offset that is equivalent to the amplitude of the triangular carrier signal were used to generate the PWM signals. The inverter is capable of producing seven levels of output-voltage levels from the dc supply voltage. This paper proposes a new multilevel inverter topology using an H-bridge output stage with two bidirectional auxiliary switches. The new topology produces a significant reduction in the number of power devices and capacitors required to implement a multilevel output using the asymmetric cascade configuration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20cascade%20configuration" title="asymmetric cascade configuration">asymmetric cascade configuration</a>, <a href="https://publications.waset.org/abstracts/search?q=H-Bridge" title=" H-Bridge"> H-Bridge</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20inverter" title=" multilevel inverter"> multilevel inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=Pulse%20Width%20Modulation%20%28PWM%29" title=" Pulse Width Modulation (PWM)"> Pulse Width Modulation (PWM)</a> </p> <a href="https://publications.waset.org/abstracts/1763/implementation-of-a-novel-modified-multilevel-inverter-topology-for-grid-connected-pv-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1763.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">357</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">7115</span> New Estimation in Autoregressive Models with Exponential White Noise by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman%20Suparman">Suparman Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20%28AR%29%20model" title="autoregressive (AR) model">autoregressive (AR) model</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20white%20Noise" title=" exponential white Noise"> exponential white Noise</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20Markov%20Chain%20Monte%20Carlo%20%28MCMC%29" title=" reversible jump Markov Chain Monte Carlo (MCMC)"> reversible jump Markov Chain Monte Carlo (MCMC)</a> </p> <a href="https://publications.waset.org/abstracts/71720/new-estimation-in-autoregressive-models-with-exponential-white-noise-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71720.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">355</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">7114</span> Simulating the Hot Hand Phenomenon in Basketball with Bayesian Hidden Markov Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20%20Calvo">Gabriel Calvo</a>, <a href="https://publications.waset.org/abstracts/search?q=Carmen%20Armero"> Carmen Armero</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Spezia"> Luigi Spezia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A basketball player is said to have a hot hand if his/her performance is better than expected in different periods of time. A way to deal with this phenomenon is to make use of latent variables, which can indicate whether the player is ‘on fire’ or not. This work aims to model the hot hand phenomenon through a Bayesian hidden Markov model (HMM) with two states (cold and hot) and two different probability of success depending on the corresponding hidden state. This task is illustrated through a comprehensive simulation study. The simulated data sets emulate the field goal attempts in an NBA season from different profile players. This model can be a powerful tool to assess the ‘streakiness’ of each player, and it provides information about the general performance of the players during the match. Finally, the Bayesian HMM allows computing the posterior probability of any type of streak. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernoulli%20trials" title="Bernoulli trials">Bernoulli trials</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20goals" title=" field goals"> field goals</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20variables" title=" latent variables"> latent variables</a>, <a href="https://publications.waset.org/abstracts/search?q=posterior%20distribution" title=" posterior distribution"> posterior distribution</a> </p> <a href="https://publications.waset.org/abstracts/135522/simulating-the-hot-hand-phenomenon-in-basketball-with-bayesian-hidden-markov-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135522.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">190</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">7113</span> Bayesian Approach for Moving Extremes Ranked Set Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Ali%20Al-Hadhrami">Said Ali Al-Hadhrami</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Ibrahim%20Al-Omari"> Amer Ibrahim Al-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian estimation for the mean of exponential distribution is considered using Moving Extremes Ranked Set Sampling (MERSS). Three priors are used; Jeffery, conjugate and constant using MERSS and Simple Random Sampling (SRS). Some properties of the proposed estimators are investigated. It is found that the suggested estimators using MERSS are more efficient than its counterparts based on SRS. <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=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20extreme%20ranked%20set%20sampling" title=" moving extreme ranked set sampling"> moving extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/30733/bayesian-approach-for-moving-extremes-ranked-set-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info 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