Bayesian partitioning for estimating disease risk

Biometrics

Volumn 57, Issue 1, 2001, Pages 143-149

  Denison, D G T ^a   Holmes, C C ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian computation; Leukemia incidence data; Markov chain Monte Carlo (MCMC); Point source; Spatial count data; Voronoi tessellation

Indexed keywords

DISEASES; MARKOV PROCESSES; RISK ASSESSMENT; RISK PERCEPTION;

BAYESIAN; BAYESIAN COMPUTATION; COUNT DATUM; LEUKEMIA INCIDENCE DATA; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE-CARLO; POINT-SOURCES; SPATIAL COUNT DATA; VORONOI TESSELLATIONS;

BAYESIAN NETWORKS;

ARTICLE; BAYES THEOREM; DATA ANALYSIS; DISEASE PREDISPOSITION; REGRESSION ANALYSIS; STATISTICAL MODEL; SYSTEM ANALYSIS;

EID: 0035106734     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2001.00143.x     Document Type: Article

References (18)

1. Bayesian image restoration, with two applications in spatial statistics
2. Empirical Bayes estimates of age-standardized relative risks for use in disease mapping
3. Bayesian inference for generalized linear and proportional hazards models via Gibbs sampling
4. Regression modelling of disease risk in relation to point sources
5. Sampling-based approaches to calculating marginal densities
6. Modelling categorical covariates in Bayesian disease mapping by partition structures
7. Computing Dirichlet tessellations in the plane
8. Monte Carlo sampling methods using Markov chains and their applications
9. Non-parametric Bayesian estimation of a spatial Poisson intensity
10. Modeling a Poisson forest in variable elevation: A nonparametric Bayesian approach
11. Bayesian wavelet networks for nonparametric regression
12. Bayes factors
13. Bayesian detection of clusters and discontinuities in disease maps
14. On the analysis of mortality events associated with a prespecified fixed point
15. Assessment of disease risk in relation to a pre-specified source
16. Spatial pattern analyses to detect rare disease clusters