Cluster detection using Bayes factors from overparameterized cluster models |
| |
Authors: | Ronald Gangnon Murray K Clayton |
| |
Institution: | (1) Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706-1461, USA;(2) Departments of Statistics and Plant Pathology, University of Wisconsin–Madison, Madison, WI 53706-1461, USA |
| |
Abstract: | In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters.
Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte
Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions
to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local
Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data
on breast cancer incidence in Wisconsin. |
| |
Keywords: | Bayes factor Cluster detection Random effects Reversible jump Markov chain Monte Carlo Spatial epidemiology |
本文献已被 SpringerLink 等数据库收录! |
|