JSM 2005 Online Program

Abstract #304598

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	348
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #304598
Title:	Bayesian Analysis for the Multinomial Probit Model
Author(s):	Xiao Zhang*+ and John Boscardin and Thomas R. Belin
Companies:	University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
Address:	3281 Sepulveda BLVD, Los Angeles, CA, 90034, United States
Keywords:	multinomial probit model ; parameter extension ; Gibbs sampling
Abstract:	We present the multinomial probit (MNP) model in detail for both univariate and repeated nominal data. Simulations from the posterior distribution of the univariate MNP model parameters are generated using Gibbs sampling and a parameter extension algorithm described in Zhang, Boscardin, and Belin (2004). We further generalize the sampling algorithm of the MNP model to handle repeated nominal data. The Bayesian method we propose can both avoid the parameter identification problems and allow flexible prior distributions on the covariance matrix of the utility vectors. We illustrate our methodology using three simulated examples and an application to data from a cancer prevention and control study.

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