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.
Translated title of the contribution | A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling |
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Original language | English |
Pages (from-to) | 809 - 821 |
Number of pages | 13 |
Journal | Environmetrics |
Volume | 18 (7) |
DOIs | |
Publication status | Published - Nov 2007 |