Bayesian partitioning for estimating disease risk.
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.
Denison
D G
DG
Department of Mathematics, Imperial College, London, UK. d.denison@ic.ac.uk
Holmes
C C
CC
D001499:Bayes Theorem; D001699:Biometry; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004194:Disease / Q000209:etiology*; D006256:Hazardous Waste / Q000009:adverse effects; D006801:Humans; D007938:Leukemia / Q000453:epidemiology / Q000209:etiology; D008390:Markov Chains; D009010:Monte Carlo Method; D009518:New York / Q000453:epidemiology; D017711:Nonlinear Dynamics; D011336:Probability; D012306:Risk
D006256:Hazardous Waste