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Approximate cross‐validatory predictive checks in disease mapping models
Author(s) -
Marshall E. C.,
Spiegelhalter D. J.
Publication year - 2003
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1403
Subject(s) - markov chain monte carlo , computer science , bayesian probability , markov chain , hierarchical database model , simple (philosophy) , replication (statistics) , random effects model , bayesian hierarchical modeling , algorithm , bayesian inference , data mining , statistics , artificial intelligence , machine learning , mathematics , medicine , meta analysis , philosophy , epistemology
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. Copyright © 2003 John Wiley & Sons, Ltd.