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