A Bayesian multivariate probit for ordinal data with semiparametric random-effects

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• 作者：Jung Seek Kim^a ; ^{jkim@bloomu.edu} ; Brian T. Ratchford^b ; ¹ ; ^{btr051000@utdallas.edu}
• 关键词：Ordinal scale ; Response style ; Multivariate ordered probit ; Bayesian semiparametrics ; Dirichlet process ; Markov chain Monte Carlo
• 刊名：Computational Statistics and Data Analysis
• 出版年：2013
• 出版时间：August, 2013
• 年：2013
• 卷：64
• 期：Complete
• 页码：192-208
• 全文大小：723 K

文摘

A heterogeneous thresholds probit for ordered ratings is developed to remove conditional independence among responses and incorporate respondent traits. We propose a semiparametric approach to relaxing normality of random-effects in the probit model that account for differences in response style. Simulation studies provide evidence of the ability for the proposed semiparametric model to better recover an underlying distribution of respondent effects than the parametric one with a normal hierarchical prior. The application to ratings on the value of information sources for automobiles demonstrates significant correlations among responses and irregularity in the shape of unobserved heterogeneity.