Impact of prior choice on local Bayes factors for cluster detection.
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.
Gangnon
Ronald E
RE
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 610 N. Walnut Street, Madison, Wisconsin 53726, USA. ronald@biostat.wisc.edu
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000453:epidemiology; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D014922:Wisconsin / Q000453:epidemiology