Bayesian model checking for multivariate outcome data

Abstract

Bayesian models are increasingly used to analyze complex multivariate outcome data. However, diagnostics for such models have not been well developed. We present a diagnostic method of evaluating the fit of Bayesian models for multivariate data based on posterior predictive model checking (PPMC), a technique in which observed data are compared to replicated data generated from model predictions. Most previous work on PPMC has focused on the use of test quantities that are scalar summaries of the data and parameters. However, scalar summaries are unlikely to capture the rich features of multivariate data. We introduce the use of dissimilarity measures for checking Bayesian models for multivariate outcome data. This method has the advantage of checking the fit of the model to the complete data vectors or vector summaries with reduced dimension, providing a comprehensive picture of model fit. An application with longitudinal binary data illustrates the methods.