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Cluster detection using Bayes factors from overparameterized cluster models
- Source :
- Environmental and Ecological Statistics. 14:69-82
- Publication Year :
- 2007
- Publisher :
- Springer Science and Business Media LLC, 2007.
-
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.
- Subjects :
- Statistics and Probability
Inference
Spatial epidemiology
Regional Disease
Bayes factor
Reversible-jump Markov chain Monte Carlo
computer.software_genre
Random effects model
Statistics
Cluster (physics)
Data mining
Statistics, Probability and Uncertainty
computer
General Environmental Science
Mathematics
Incidence (geometry)
Subjects
Details
- ISSN :
- 15733009 and 13528505
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- Environmental and Ecological Statistics
- Accession number :
- edsair.doi...........5a9a540d04f277dd1cfa0b4da00579a0