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A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling
Author(s) -
Jang Myoung Jin,
Lee Youngjo,
Lawson Andrew B.,
Browne William J.
Publication year - 2007
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.877
Subject(s) - bayesian probability , prior probability , statistics , marginal likelihood , econometrics , inference , bayesian inference , random effects model , bayesian hierarchical modeling , variance (accounting) , covariate , hierarchical database model , mathematics , computer science , artificial intelligence , data mining , medicine , meta analysis , accounting , business
Abstract Recently Bayesian methods have been widely used in disease mapping. Hierarchical (h‐) likelihood methods allow reliable likelihood inference in random‐effect models and it is therefore interesting to compare h‐likelihood and Bayesian methods. For comparison we consider three examples: low birth weight and cancer mortality data in South Carolina and lip cancer data in Scotland. Mean estimates from both h‐likelihood and Bayesian approaches are almost identical, while variance‐component estimates can be somewhat different, depending upon choice of priors. Copyright © 2007 John Wiley & Sons, Ltd.