Journal of Probability and Statistics
Volume 2012 (2012), Article ID 617678, 26 pages
http://dx.doi.org/10.1155/2012/617678
Research Article

Bayesian Approach to Zero-Inflated Bivariate Ordered Probit Regression Model, with an Application to Tobacco Use

Shiferaw Gurmu1 and Getachew A. Dagne2

1Department of Economics, Andrew Young School of Policy Studies, Georgia State University, P.O. Box 3992, Atlanta, GA 30302, USA
2Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USA

Received 13 July 2011; Revised 18 September 2011; Accepted 2 October 2011

Academic Editor: Wenbin Lu

Copyright © 2012 Shiferaw Gurmu and Getachew A. Dagne. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a Bayesian analysis of bivariate ordered probit regression model with excess of zeros. Specifically, in the context of joint modeling of two ordered outcomes, we develop zero-inflated bivariate ordered probit model and carry out estimation using Markov Chain Monte Carlo techniques. Using household tobacco survey data with substantial proportion of zeros, we analyze the socioeconomic determinants of individual problem of smoking and chewing tobacco. In our illustration, we find strong evidence that accounting for excess zeros provides good fit to the data. The example shows that the use of a model that ignores zero-inflation masks differential effects of covariates on nonusers and users.