JSM 2004 - Toronto

Abstract #300616

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Activity Number: 16
Type: Topic Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300616
Title: Spatial Cluster Detection Using Bayes Factors from Overparameterized Models
Author(s): Ronald Gangnon*+ and Murray Clayton
Companies: University of Wisconsin, Madison and University of Wisconsin, Madison
Address: 207 WARF Office Bldg., Madison, WI, 23726,
Keywords: spatial clustering ; partition models ; Bayes factors ; Markov chain Monte Carlo
Abstract:

We consider a partition model for estimation of regional disease rates and for detection of spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained using a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. As an alternative, we consider models with a fixed, but overly large, number of partitions. We explore the ability of these models to provide informal inferences about the number and locations of clusters using localized Bayes factors. We illustrate these two approaches using two datasets from the literature (the New York leukemia data and the North Carolina SIDS data) as well as data on breast cancer incidence in Wisconsin.

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