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.
Department of Epidemiology and Public Health, Imperial College of Science, Technology and Medicine, Norfolk Place, London W2 1PG, UK. c.marshall@ic.ac.uk
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Spiegelhalter
D001499:Bayes Theorem; D006801:Humans; D008048:Lip Neoplasms / Q000453:epidemiology; D015233:Models, Statistical; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012606:Scotland / Q000453:epidemiology; D017062:Small-Area Analysis