Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes

Computational Statistics and Data Analysis

Volumn 51, Issue 6, 2007, Pages 3197-3212

  Congdon, Peter ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Adaptive; Bayesian; Convolution prior; Illness; Mixture; Mortality; Relative risk; Spatial

Indexed keywords

COMPUTATIONAL METHODS; DATA REDUCTION; HEALTH CARE; MIXTURES; PARAMETER ESTIMATION; RISK ASSESSMENT; STATISTICAL METHODS;

BAYESIAN ESTIMATION; MIXTURE MODELS; RELATIVE RISK;

ADAPTIVE SYSTEMS;

EID: 33846626960     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.11.028     Document Type: Article

References (28)

1. Banerjee S., Carlin B., and Gelfand A. Hierarchical Modeling and Analysis for Spatial Data (2004), Chapman & Hall/CRC Press, Boca Raton, Fl
2. Bernardinelli L., Clayton D., and Montomoli C. Bayesian estimates of disease maps: how important are priors?. Statist. Med. 14 (1995) 2411-2431
3. Besag J., York J., and Mollié A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Statist. Math. 43 (1991) 1-20
4. Besag J., Green P., Higdon D., and Mengersen K. Bayesian computation and stochastic systems. Statist. Sci. 10 (1995) 3-66
5. Chib S. Calculating posterior distributions and modal estimates in Markov mixture models. J. Econometrics 75 (1996) 79-97
6. Congdon P. A Bayesian approach to prediction using the gravity model, with an application to patient flow modelling. Geographical Anal. 32 (2000) 205-224
7. Congdon P. Bayesian predictive model comparison via parallel sampling. Comput. Statist. Data Anal. 48 (2005) 735-753
8. Fernandez C., and Green P. Modelling spatially correlated data via mixtures: a Bayesian approach. J. Roy. Statist. Soc. Ser. B 64 (2002) 805-826
9. Gelfand A., and Smith A. Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85 (1990) 398-409
10. Gelman A., Carlin J., Stern H., and Rubin D. Bayesian Data Analysis. second ed (2003), CRC Press, Boca Raton
11. Greenland S. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes in case-control studies. Amer. J. Epid. 160 (2004) 301-305
12. Knorr-Held L. Bayesian modelling of inseparable space-time variation in disease risk. Statist. Med. 19 (2000) 2555-2567
13. Knorr-Held L., and Rasser G. Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56 (2000) 13-21
14. Langford I., Leyland A., Rasbash J., and Goldstein H. Multilevel modelling of the geographical distributions of rare diseases. J. Roy. Statist. Soc. Ser. C 48 (1999) 253-268
15. Lawson A., and Clark A. Spatial mixture relative risk models applied to disease mapping. Statist. Med. 21 (2002) 359-370
16. Leyland A., Langford I., Rasbash J., and Goldstein H. Multivariate spatial models for event data. Statist. Med. 19 (2000) 2469-2478
17. McCulloch R., and Tsay R. Statistical analysis of economic time series via Markov switching models. J. Time Series Anal. 15 (1994) 523-539
18. Meng, X.-L., Vaida, F., 2007. What's missing for DIC with missing data? Bayesian Anal. 2, forthcoming.
19. Militino A., Ugarte M., and Dean C. The use of mixture models for identifying high risks in disease mapping. Statist. Med. 20 (2001) 2035-2049
20. Mollié A. Bayesian mapping of disease. In: Gilks W., Richardson S., and Spieglehalter D. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall, London (Chapter 20)
21. Pourahmadi M., and Daniels M. Dynamic conditionally linear mixed models for longitudinal data. Biometrics 58 (2002) 225-231
22. Raftery A. Approximate Bayes factors and accounting for model uncertainty in generalized linear models. Biometrika 83 (1996) 251-266
23. Sahu S., Dey D., and Branco M. A new class of multivariate skew distributions with applications to Bayesian regression models. Canad. J. Statist. 31 (2003) 129-150
24. Shively T., Kohn R., and Wood S. Variable selection and function estimation in additive nonparametric regression using a data-based prior. J. Amer. Statist. Assoc. 94 (1999) 777-794
25. Spiegelhalter D., Best N., Carlin B., and van der Linde A. Bayesian measures of model complexity and fit. J. Roy. Statist. Soc. Ser. B 64 (2002) 583-639
26. Spiegelhalter, D., Thomas, A., Best, N., Lunn, D., 2005. WinBUGS User Manual, Version 2.10, April 2005. 〈http://www.mrc-bsu.cam.ac.uk/bugs〉.
27. Stern H., and Cressie N. Posterior predictive model checks for disease mapping models. Statist. Med. 19 (2000) 2377-2397
28. Yau P., Kohn R., and Wood S. Bayesian variable selection and model averaging in high dimensional multinomial nonparametric regression. J. Comput. Graph. Statist. 12 (2003) 23-54