Statistics Seminars: Bayesian inference for multivariate ordinal data
17 June 2005 00:00 in CM107
"Methods for investigating the structure in contingency tables are typically based on determining appropriate log-linear models for the classifying variables. Where one or more of the variables is ordinal, such models do not take this property into account. In this talk, I describe how the multivariate probit model (Chib and Greenberg, 1998) can be adapted so that ordinal data models can be compared using Bayesian methods. By a suitable choice of parameterisation, the conditional posterior distributions are standard and are easily simulated from, and reversible jump Markov chain Monte Carlo computation can be used to estimate posterior model probabilities for undirected decomposable graphical models. The approach is illustrated with two examples."
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