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.

Original publication

DOI

10.1111/j.0006-341x.2001.00143.x

Type

Journal article

Journal

Biometrics

Publication Date

03/2001

Volume

57

Pages

143 - 149

Addresses

Department of Mathematics, Imperial College, London, UK. d.denison@ic.ac.uk

Keywords

Humans, Leukemia, Disease, Cluster Analysis, Data Interpretation, Statistical, Monte Carlo Method, Probability, Bayes Theorem, Markov Chains, Risk, Hazardous Waste, Biometry, Nonlinear Dynamics, New York