Bayesian analysis of realistically complex models

Journal of the Royal Statistical Society. Series A: Statistics in Society

Volumn 159, Issue 2, 1996, Pages 323-342

  Best, N G ^a,b   Spiegelhalter, D J ^a   Thomas, A ^a   Brayne, C E G ^a  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Conditional independence; Gibbs sampling; Graphical models; Informative drop out; Markov chain Monte Carlo; Ordered categorical data; Random effects models

Indexed keywords

EID: 0041374864     PISSN: 09641998     EISSN: None     Source Type: Journal    
DOI: 10.2307/2983178     Document Type: Article

References (33)

1. Agresti, A. (1989) A survey of models for repeated ordered categorical response data. Statist. Med., 8, 1209-1224.
2. Anderson, J. A. and Philips, P. R. (1981) Regression, discrimination and measurement models for ordered categorical variables. Appl. Statist., 30, 22-31.
3. Anthony, J. C., LeResche, L. A., Niaz, U., von Korff, M. and Folstein, M. F. (1982) Limits of the Mini-Mental State as a screening test for dementia and delirium among hospital patients. Psychol. Med., 12, 397-408.
4. Arminger, G., Clogg, C. C. and Sobel, M. E. (eds) (1995) Handbook of Statistical Modelling for the Social and Behavioural Sciences. New York: Plenum.
5. Best, N. G., Cowles, M. K. and Vines, S. K. (1995) CODA: Convergence Diagnosis and Output Analysis Software for Gibbs Sampling Output, Version 0.3. Cambridge: Medical Research Council Biostatistics Unit.
6. Brayne, C. and Calloway, P. (1990) The case identification of dementia in the community: a comparison of methods. Int. J. Ger. Psychiat., 5, 309-316.
7. Bryk, A. S. and Raudenbush, S. W. (1992) Hierarchical Linear Models. Newbury Park: Sage.
8. Carlin, B. P. and Chib, S. (1995) Bayesian model choice via Markov chain Monte Carlo methods. J. R. Statist. Soc. B, 57, 473-484.
9. Casella, G. and George, E. I. (1992) Explaining the Gibbs sampler. Am. Statistn, 46, 167-174.
10. Cowles, M. K. and Carlin, B. P. (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. J. Am. Statist. Ass., to be published.
11. Diggle, P. and Kenward, M. G. (1994) Informative drop-out in longitudinal data analysis (with discussion). Appl. Statist., 43, 49-93.
12. Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975) Mini-Mental State: a practical method for grading the cognitive stages of patients for the clinician. J. Psychiat. Res., 12, 189-198.
13. Gelfand, A. E. and Dey, D. K. (1994) Bayesian model choice: asymptotics and exact calculations. J. R. Statist. Soc. B, 56, 501-514.
14. Gelfand, A. E. and Smith, A. F. M. (1990) Sampling-based approaches to calculating marginal densities. J. Am. Statist. Ass., 85, 398-409.
15. Gelman, A. and Rubin, D. B. (1992) Inference from iterative simulation using multiple sequences. Statist. Sci., 7, 457-472.
16. Gilks, W. R., Clayton, D. G., Spiegelhalter, D. J., Best, N. G., McNeil, A. J., Sharples, L. D. and Kirby, A. J. (1993) Modelling complexity: applications of Gibbs sampling in medicine. J. R. Statist. Soc. B, 55, 39-52.
17. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1995) Markov Chain Monte Carlo in Practice. New York: Chapman and Hall.
18. Gilks, W. R., Thomas, A. and Spiegelhalter, D. J. (1994) A language and program for complex Bayesian modelling. Statistician, 43, 169-178.
19. Goldstein, H. (1995) Multilevel Models in Educational and Social Research. London: Arnold.
20. Goldstein, H. and Spiegelhalter, D. J. (1996) League tables and their limitations: statistical issues in comparisons of institutional performance (with discussion). J. R. Statist. Soc. A, 159, in the press.
21. Hedeker, D. and Gibbons, R. D. (1994) A random-effect ordinal regression model for multilevel analysis. Biometrics, 50, 933-944.
22. Longford, N. T. (1993) Random Coefficient Models. Oxford: Oxford University Press.
23. McCullagh, P. (1980) Regression models for ordinal data (with discussion). J. R. Statist. Soc. B, 42, 109-142.
24. Muir, M. S. and Brayne, C. (1992) A longitudinal study in progress: a five year follow-up of women aged 70-79 years living in a rural community. Neuroepidemiology, 11, 67-70.
25. Rodríguez, G. and Goldman, N. (1995) An assessment of estimation procedures for multilevel models with binary responses. J. R. Statist. Soc. A, 158, 73-89.
26. Roth, M., Tym, E., Mountjoy, C. Q., Huppert, F. A., Hendrie H., Verma, S. and Goddard, R. (1986) CAMDEX: a standardised instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. J. Psychiat., 149, 698-709.
27. Spiegelhalter, D. J., Thomas, A., Best, N. G. and Gilks, W. R. (1995) BUGS Manual and Examples: Version 0.50. Cambridge: Medical Research Council Biostatistics Unit.
28. Statistical Sciences (1991) S-PLUS User's Manual. Seattle: Statistical Sciences.
29. Stram, D. O., Wei, L. J. and Ware, J. H. (1988) Analysis of repeated ordered categorical outcomes with possibly missing observations and time-dependent covariates. J. Am. Statist. Ass., 83, 631-637.
30. Wakefield, J. C., Smith, A. F. M., Racine-Poon, A. and Gelfand, A. E. (1994) Bayesian analysis of linear and non-linear population models by using the Gibbs sampler. Appl. Statist., 43, 201-221.
31. Ware, J. H., Lipsitz, S. and Speizer, F. E. (1988) Issues in the analysis of repeated categorical outcomes. Statist. Med., 7, 95-107.
32. Wedderburn, R. W. M. (1974) Quasi-likelihood functions, generalized linear models and the Gauss-Newton method. Biometrika, 61, 439-447.
33. Whittaker, J. (1990) Graphical Models in Applied Multivariate Statistics. Chichester: Wiley.