Bayesian selection of log‐linear models

Abstract

AbstractA general methodology is presented for finding suitable Poisson log‐linear models with applications to multiway contingency tables. Mixtures of multivariate normal distributions are used to model prior opinion when a subset of the regression vector is believed to be nonzero. This prior distribution is studied for two‐ and three‐way contingency tables, in which the regression coefficients are interpretable in terms of odds ratios in the table. Efficient and accurate schemes are proposed for calculating the posterior model probabilities. The methods are illustrated for a large number of two‐way simulated tables and for two three‐way tables. These methods appear to be useful in selecting the best log‐linear model and in estimating parameters of interest that reflect uncertainty in the true model.