Approximate cross-validatory predictive checks in disease mapping models.

When fitting complex hierarchical disease mapping models, it can be important to identify regions that diverge from the assumed model. Since full leave-one-out cross-validatory assessment is extremely time-consuming when using Markov chain Monte Carlo (MCMC) estimation methods, Stern and Cressie consider an importance sampling approximation. We show that this can be improved upon through replication of both random effects and data. Our approach is simple to apply, entirely generic, and may aid the criticism of any Bayesian hierarchical model.