A Bayesian Approach to Spatial Correlations in the Multivariate Probit Model

Jervyn Ang successfully defended his M.Sc. thesis entitled "A Bayesian Approach to Spatial Correlations in the Multivariate Probit Model" on 2010-10-27.

Ordered categorical data arise in many applied settings. For example, many surveys have responses that may be restricted to ``Strongly Disagree", ``Disagree”, ``Neutral", ``Agree", and ``Strongly Agree". Here, the responses are ordinal variables. That is, the agreeability of respondents to questions have relative ranks, but there is no measure of exact magnitude like there is with continuous variables.

In many scenarios, questions may have correlated responses. As well, different respondents may be spatially or otherwise correlated. Probit models are a means to using normal latent variables in modeling ordinal responses. In this project, we take a Bayesian approach to including both`between question’ and `between respondent’ correlations in a multivariate probit model. We discuss the efficacy of this spatial multivariate probit model.

This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Jervyn Ang (jaa4sfu.ca) or his supervisor Derek Bingham (dbingham@stat.sfu.ca), Department of Statistics and Actuarial Science,

2010-10-27