March 4 (Wed) 1 p.m.
Alavi Commons Room, 6625 Everett Tower

Bayesian Nonparametric Multivariate Ordinal Regression

Junshu Bao
University of South Carolina

Multivariate ordinal data are modeled as a finite stick-breaking mixture of multivariate probit models. Parametric multivariate probit models are first developed for ordinal data, then generalized to finite mixtures of multivariate probit models. Specific recommendations for prior settings are found to work well in simulations and data analyses. Interpretation of the model is carried out by examining aspects of the mixture components as well as through averaged effects focusing on the mean responses. A simulation verifies that the fitting technique works, and an analysis of alcohol drinking behavior data illustrates the usefulness of the proposed model.

All statistics students are expected to attend.


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Department of Statistics
3304 Everett Tower
Western Michigan University
Kalamazoo MI 49008-5152 USA
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