Journal
BIOMETRICS
Volume 57, Issue 1, Pages 143-149Publisher
WILEY
DOI: 10.1111/j.0006-341X.2001.00143.x
Keywords
Bayesian computation; leukemia incidence data; Markov chain Monte Carlo (MCMC); point source; spatial count data; Voronoi tessellation
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available