Bayesian partitioning for estimating disease risk.

Journal article

This paper presents a Bayesian nonlinear approach for the analysis of spatial count data. It extends the Bayesian partition methodology of Holmes, Denison, and Mallick (1999, Bayesian partitioning for classification and regression, Technical Report, Imperial College, London) to handle data that involve counts. A demonstration involving incidence rates of leukemia in New York state is used to highlight the methodology. The model allows us to make probability statements on the incidence rates around point sources without making any parametric assumptions about the nature of the influence between the sources and the surrounding location.

Authors: Denison, D; Holmes, C

• Publication status: Published
• Journal: Biometrics
• Volume: 57
• Issue: 1
• Pages: 143-149
• Publication date: 2001-03-01
• DOI: 10.1111/j.0006-341x.2001.00143.x
• EISSN: 1541-0420
• ISSN: 0006-341X
• Language: English
• Keywords: Probability; Biometry; Humans; Monte Carlo Method; Disease; New York; Risk; Bayes Theorem; Markov Chains; Leukemia; Nonlinear Dynamics; Data Interpretation, Statistical; Hazardous Waste; Cluster Analysis