Impact of prior choice on local Bayes factors for cluster detection

Abstract

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.