Bayesian model comparison and model averaging for small-area estimation

Annals of Applied Statistics

Volumn 3, Issue 1, 2009, Pages 199-221

  Aitkin, Murray ^a,b   Liu, Charles C ^a,b   Chadwick, Tom ^a,b  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Cancer rates; Deviance distributions; Deviance information criterion; Model averaging; Model choice; Posterior shrinkage; Small area estimation

Indexed keywords

EID: 78049345547     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/08-AOAS205     Document Type: Article

References (16)

1. Aitkin, M. (1997). The calibration of P-values, posterior Bayes factors and the AIC from the posterior distribution of the likelihood (with discussion). Statist. Comput. 7 253-272.
2. Aitkin, M. (1999). A general maximum likelihood analysis of variance components in generalized linear models. Biometrics 55 117-128.
3. Aitkin, M., Boys, R. J. and Chadwick, T. (2005). Bayesian point null hypothesis testing via the posterior likelihood ratio. Statist. Comput. 15 217-230.
4. Carlin, B. P. and Louis, T. A. (1996). Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall, London.
5. Celeux, G., Forbes, F., Robert, C. P. and Titterington, D. M. (2006). Deviance information criteria for missing data models (with discussion). Bayesian Anal. 1 651-706.
6. Congdon, P. (2005). Bayesian predictive model comparison via parallel sampling. Comput. Statist. Data Anal. 48 735-753.
7. Congdon, P. (2006). Bayesian model comparison via parallel model output. J. Statist. Comput. Simul. 76 149-165.
8. Dempster, A. P. (1974). The direct use of likelihood in significance testing. InProc. Conf. Foundational Questions in Statistical Inference (O. Barndorff-Nielsen, P. Blaesild and G. Sihon, eds.) 335-352. Kluwer, Hingham, MA.
9. Dempster, A. P. (1997). The direct use of likelihood in significance testing. Statist. Comput. 7 247-252.
10. Fox, J.-P. (2005). Multilevel IRT using dichotomous and polytomous response data. Brit. J. Math. Statist. Psych. 58 145-172.
11. Hoeting, J. A., Madigan, D., Raftery, A. and Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statist. Sci. 14 382-417.
12. Ridall, P. G., Pettitt, A. N., Friel, N., Henderson, R. and McCombe, P. (2007). Motor unit number estimation using reversible jump Markov chain Monte Carlo methods (with discussion). J. Roy. Statist. Soc. Ser. C 56 235-269.
13. Roeder, K. (1990). Density estimation with confidence sets exemplified by superclusters and voids in the galaxies. J. Amer. Statist. Assoc. 85 617-624.
14. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). J. Roy. Statist. Soc. Ser. B 64 583-639.
15. Trevisani, M. and Gelfand, A. E. (2003). Inequalities between expected marginal log likelihoods with implications for likelihood-based model comparison. Canadian J. Statist. 31 239-250.
16. Tsutakawa, R. K. (1985). Estimation of cancer mortality rates: A Bayesian analysis of small frequencies. Biometrics 41 69-79.