Bayesian Partitioning for Estimating Disease Risk
- 1 March 2001
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 57 (1) , 143-149
- https://doi.org/10.1111/j.0006-341x.2001.00143.x
Abstract
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.Keywords
This publication has 17 references indexed in Scilit:
- Bayesian Detection of Clusters and Discontinuities in Disease MapsBiometrics, 2000
- Bayesian wavelet networks for nonparametric regressionIEEE Transactions on Neural Networks, 2000
- Non-parametric Bayesian Estimation of a Spatial Poisson IntensityScandinavian Journal of Statistics, 1998
- Regression Modelling of Disease Risk in Relation to Point SourcesJournal of the Royal Statistical Society Series A: Statistics in Society, 1997
- Bayes FactorsJournal of the American Statistical Association, 1995
- Bayes FactorsJournal of the American Statistical Association, 1995
- Bayesian Inference for Generalized Linear and Proportional Hazards Models via Gibbs SamplingJournal of the Royal Statistical Society Series C: Applied Statistics, 1993
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Monte Carlo sampling methods using Markov chains and their applicationsBiometrika, 1970