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Bayesian Partitioning for Estimating Disease Risk
Author(s) -
Denison D. G. T.,
Holmes C. C.
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00143.x
Subject(s) - bayesian probability , computer science , partition (number theory) , statistics , parametric statistics , econometrics , bayesian inference , data mining , bayesian statistics , machine learning , artificial intelligence , mathematics , combinatorics
Summary. 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.