This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 33
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306985
Title: A Semiparametric Bayesian Approach for Modeling Ordinal Survey Data
Author(s): Saman Muthukumarana*+ and Tim B. Swartz
Companies: Simon Fraser University and Simon Fraser University
Address: Department of Statistic and Actuarial Science, Burnaby, BC, V5A1S6, Canada
Keywords: Dirichlet process ; latent variables ; MCMC
Abstract:

This paper presents a Bayesian latent variable model to analyze ordinal response survey data. The ordinal response data are viewed as multivariate responses arising from class of continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distribution. The key aspect of the Dirichlet process in our application is that the personality trait parameters have support on a discrete space and this enables the clustering of personality types. The proposed mechanism adjusts for classes of personality traits. As the resulting posterior distribution is complex and high-dimensional, posterior expectations are approximated by MCMC methods. As a by-product of the proposed methodology, we attempt to identify areas where performance has been poor or exceptional.


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