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Impact of prior choice on local Bayes factors for cluster detection
Author(s) -
Gang Ronald E.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2410
Subject(s) - bayes' theorem , bayes factor , computer science , cluster analysis , robustness (evolution) , cluster (spacecraft) , inference , statistics , econometrics , data mining , bayesian probability , artificial intelligence , machine learning , mathematics , biology , biochemistry , gene , programming language
In this paper, we evaluate the usefulness of local Bayes factors as a tool for spatial cluster detection. In particular, we consider whether local Bayes factors from models with a fixed, but overly large number of clusters can consistently identify the evidence for clustering for a variety of prior specifications for the cluster locations. We also investigate the robustness of the local Bayes factor to the number of clusters included in the model. We explore the impacts of prior choice for cluster location and the number of clusters on posterior inference for disease rates. We conduct the comparison by analysing data on 1990 breast cancer incidence in Wisconsin. Copyright © 2006 John Wiley & Sons, Ltd.