Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion
- 1 May 2008
- journal article
- Published by Taylor & Francis Ltd in Technometrics
- Vol. 50 (2), 182-191
- https://doi.org/10.1198/004017008000000064
Abstract
Multivariate ordinal data arise in many applications. This article proposes a new, efficient method for Bayesian inference for multivariate probit models using Markov chain Monte Carlo techniques. The key idea is the novel use of parameter expansion to sample correlation matrices. A nice feature of the approach is that inference is performed using straightforward Gibbs sampling. Bayesian methods for model selection are also discussed. Our approach is motivated by a study of how women make decisions on taking medication to reduce the risk of breast cancer. Furthermore, we compare and contrast the performance of our approach with other methods.Keywords
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