Bayesian model comparison via parallel model output

Journal of Statistical Computation and Simulation

Volumn 76, Issue 2, 2006, Pages 149-165

  Congdon, Peter ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Model averaging; Model choice; Model probabilities; Model weights; Parallel sampling

Indexed keywords

EID: 30944448669     PISSN: 00949655     EISSN: None     Source Type: Journal    
DOI: 10.1080/00949650412331320864     Document Type: Article

References (29)

1. Gelfand, A. and Smith, A., 1990, Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.
2. Best, N., Spiegelhalter, D., Thomas, A. and Brayne, C., 1996, Bayesian analysis of realistically complex models. Journal of the Royal Statistical Society, Series A, 159, 323-342.
3. Burnham, K. and Anderson, D., 2002, Model Selection and Multimodel Inference: A Practical Information -Theoretic Approach (2nd edn) (New York: Springer-Verlag).
4. Wasserman, L., 2000, Bayesian model selection and model averaging. Journal of Mathematical Psychology, 44, 92-107.
5. Carlin, B. and Chib, S., 1995, Bayesian model choice via the Markov Chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57, 473-484.
6. Hoeting, J., Madigan, D., Raftery, A. and Volinsky, C., 1999, Bayesian model averaging: a tutorial. Statistical Science, 14, 382-401.
7. Dempster, A., 1997, The direct use of likelihood for significance testing. Statistics and Computing, 7, 247-252.
8. Spiegelhalter, D., Best, N., Carlin, B. and van der Linde, A., 2002, Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, 64, 583-639.
9. Besag, J., 1989, Towards Bayesian image analysis. Journal of Applied Statistics, 16, 395-407.
10. Anderson, D., Burnham, K. and Thompson, W., 2000, Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management, 64, 912-923.
11. Brooks, S., 2002, Discussion to Spiegelhalter et al. Journal of the Royal Statistical Society, Series B, 64, 616-618.
12. Schwarz, G., 1978, Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
13. Kadane, J. and Lazar, N., 2004, Methods and criteria for model selection. Journal of the American Statistical Association, 99, 279-290.
14. Johnson, V. and Albert, J., 1999, Ordinal Data Modeling (New York: Springer).
15. Trevisani, M. and Gelfand, A., 2003, Inequalities between expected marginal log likelihoods with implications for likelihood-based model comparison. Canadian Journal of Statistics, 31, 239-250.
16. Laud, P. and Ibrahim, J., 1995, Predictive model selection. Journal of the Royal Statistical Society, 57B, 247-262.
17. Meyer, M. and Laud, P., 2002, Predictive variable selection in generalized linear models. Journal of the American Statistical Association, 97, 859-871.
18. Carlin, B. and Louis, T., 2001, Bayes and Empirical Bayes Methods for Data Analysis (2nd edn) (Boca Raton, FL: Chapman and Hall/CRC Press).
19. Gelman, A., Carlin, J., Stern, H. and Rubin, D., 2003, Bayesian Data Analysis (2nd edn) (Boca Raton, FL: CRC Press).
20. Wagenmakers, E. and Farrell, S., 2004, AIC model selection using Akaike weights. Psychonomic Bulletin and Review, 11, 192-196.
21. Hald, A., 1952, Statistical Theory with Engineering Applications (New York: Wiley).
22. Casella, G. and Moreno, E., 2002, Objective Bayesian variable selection, University of Florida (manuscript). Available online at: www.stat.ufl.edu/~casella/class/variableSelectionTalk.pdf.
23. Bartlett, M., 1957, A comment on D. V. Lindley's statistical paradox. Biometrika, 44, 533-534.
24. Lindley, D., 1957, A statistical paradox. Biometrika, 44, 187-192.
25. Francis, B., Green, M. and Payne, C. (Eds), 1993, The GLIM System, Release 4 Manual (Oxford: Clarendon Press).
26. Fitzmaurice, G. and Laird, N., 1993, A likelihood-based method for analysing longitudinal binary responses. Biometrika, 80, 141-151.
27. Congdon, P., 2001, Predicting adverse infant health outcomes using routine screening variables: modelling the impact of interdependent risk factors. Journal of Applied Statistics, 28, 183-197.
28. Congdon, P., 2005, Bayesian predictive model comparison via parallel sampling. Computational Statistics and Data Analysis, in press.
29. Kass, R. and Raftery, A., 1995, Bayes factors. Journal of the American Statistical Association, 90, 773-795.