Journal
COMPUTATIONAL STATISTICS
Volume 26, Issue 1, Pages 159-179Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-010-0215-3
Keywords
Empirical Bayes; Fully Bayes; Disease mapping; Hierarchical generalized linear model; Hierarchical likelihood; Prediction
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In disease mapping, the Bayesian approach is widely used for forming the prediction interval of relative risks. In this paper we propose a hierarchical-likelihood interval for disease mapping, which accounts for the inflation of standard error estimates caused by uncertainty in the estimation of the fixed parameters. Comparison is made with the Bayesian prediction intervals derived from penalized quasi-likelihood and fully Bayesian methods. Through simulation studies, we show that prediction intervals for random effects using hierarchical likelihood maintains the required level.
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