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A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling
10.1002/env.877.abs
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.
A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling
10.1002/env.877.abs
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.
A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling
Jang, Myoung Jin (author) / Lee, Youngjo (author) / Lawson, Andrew B. (author) / Browne, William J. (author)
Environmetrics ; 18 ; 809-821
2007-11-01
13 pages
Article (Journal)
Electronic Resource
English
Hierarchical Bayesian approaches to statistical modelling of geotechnical data
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