Abstract #301462

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JSM 2002 Abstract #301462
Activity Number: 19
Type: Contributed
Date/Time: Sunday, August 11, 2002 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #301462
Title: Bayesian Inference for Multivariate Ordinal and Binary Data
Author(s): Earl Lawrence*+
Affiliation(s): University of Michigan
Address: 4062 Frieze Building, Ann Arbor, Michigan, 48109-1285, USA
Keywords: multivariate probit ; correlated ordinal data
Abstract:

We consider situations where multiple sets of ordinal data are observed from the same experimental unit. We develop Bayesian inference using a multivariate probit regression model. An underlying latent variable framework is used to capture the dependence among the multivariate data. This model includes the special case in which one or more sets of the data are binary. By a judicious choice of priors, we are able to overcome some of the computational difficulties of other Bayesian approaches in the literature. An application to multivariate probe test data from integrated circuit fabrication will be described.

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Revised March 2002