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An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave‐one‐out cross‐validatory (LOOCV) model assessment is the gold standard for estimating predictive p‐values that can flag such divergent regions. However, actual LOOCV is time‐consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior...
Looking at predictive accuracy is a traditional method for comparing models. A natural method for approximating out-of-sample predictive accuracy is leave-one-out cross-validation (LOOCV)—we alternately hold out each case from a full dataset and then train a Bayesian model using Markov chain Monte Carlo without the held-out case; at last we evaluate the posterior predictive distribution of all cases...
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