Bayesian latent variable models for clustered mixed outcomes

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 62, Issue 2, 2000, Pages 355-366

  Dunson, David B ^a  

^a NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

Author keywords

Gibbs sampler; Mixture model; Multihit model; Multiple outcomes; Reproductive applications

Indexed keywords

EID: 0034360861     PISSN: 13697412     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9868.00236     Document Type: Article

References (32)

1. Albert, J. H. and Chib, S. (1993) Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Ass., 88, 669-679.
2. Arminger, G. and Küsters, U. (1988) Latent trait models with indicators of mixed measurement level. In Latent Trait and Latent Class Models (eds R. Langeheine and J. Rost), pp. 51-73. New York: Plenum
3. Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. J. Am. Statist. Ass., 88, 9-25.
4. Catalano, P. J. and Ryan, L. M. (1992) Bivariate latent variable models for clustered discrete and continuous outcomes. J. Am. Statist. Ass., 87, 651-658.
5. Chen, J. (1993) A malformation incidence dose-response model incorporating fetal weight and/or litter size as covariates. Risk Anal., 13, 559-564.
6. Chen, M. H. and Dey, D. K. (1998) Bayesian modeling of correlated binary responses via scale mixture of multivariate normal link functions. Technical Report 98. Department of Statistics, University of Connecticut, Storrs.
7. Chib, S. (1995) Marginal likelihood from the Gibbs output. J. Am. Statist. Ass., 90, 1313-1321.
8. Chib, S. and Greenberg, E. (1998) Analysis of multivariate probit models. Biometrika, 85, 347-361.
9. Chu, H. M. and Kuo, L. (1997) Sampling based approach for one-hit and multi-hit models in quantal bioassay. Statist. Comput., 7, 183-192.
10. Cowles, M. K. and Carlin, B. P. (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. J. Am. Statist. Ass., 91, 883-904.
11. Cox, D. R. and Wermuth, N. (1992) Response models for mixed binary and quantitative variables. Biometrika, 79, 441-461.
12. Dey, D. K. and Chen, M. H. (1996) Bayesian analysis of correlated binary data models. Technical Report 96-02. Department of Statistics, University of Connecticut, Storrs.
13. DiCiccio, T. J., Kass, R. E., Raftery, A. and Wasserman, L. (1997) Computing Bayes factors by combining simulation and asymptotic approximations. J. Am. Statist. Ass., 92, 903-915.
14. Fitzmaurice, G. M. and Laird, N. M. (1995) Regression models for a bivariate discrete and continuous outcome with clustering. J. Am. Statist. Ass., 90, 845-852.
15. Gelman, A., Meng, X. L. and Stern, H. (1996) Posterior predictive assessment of model fitness via realized discrepancies. Statist. Sin., 6, 733-760.
16. Gilks, W. R., Thomas, A. and Spiegelhalter, D. J. (1994) A language and program for complex Bayesian modelling. Statistician, 43, 169-177.
17. Gilks, W. R., Wang, C. C., Yvonnet, B. and Coursaget, P. (1993) Random-effects models for longitudinal data using gibbs sampling. Biometrics, 49, 441-453.
18. Gilks, W. R. and Wild, P. (1992) Adaptive rejection sampling for Gibbs sampling. Appl. Statist., 41, 337-348.
19. Hastings, W. K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
20. Kleinman, K. P. and Ibrahim, J. G. (1998) A semi-parametric Bayesian approach to generalized linear mixed models. Statist. Med., 17, 2579-2596.
21. Legler, J. M. and Ryan, L. M. (1997) Latent variable models for teratogenesis using multiple binary outcomes. J. Am. Statist. Ass., 92, 13-20.
22. Moustaki, I. (1996) A latent trait and a latent class model for mixed observed variables. Br. J. Math. Statist. Psychol., 49, 313-334.
23. Moustaki, I. and Knott, M. (1999) Generalized latent trait models. Psychometrika, to be published.
24. National Toxicology Program (1988) Ethylene glycol monomethyl ether reproduction and fertility assessment in CD-1 mice when administered in drinking water. NIH Publ. 88211446. US Department of Health and Human Services, Public Health Service, National Institutes of Health. Research Triangle Park.
25. Rai, K. and Van Ryzin, J. (1981) A generalized multihit dose-response model for low-dose extrapolation. Biometrics, 37, 341-352.
26. Sammel, M. D., Ryan, L. M. and Legler, J. M. (1997) Latent variable models for mixed discrete and continuous outcomes. J. R. Statist. Soc. B, 59, 667-678.
27. Shi, J. Q. and Lee, S. Y. (1998) Bayesian sampling-based approach for factor analysis models with continuous and polytomous data. Br. J. Math. Statist. Psychol., 51, 233-252.
28. -(2000) Latent variable models with mixed continuous and polytomous data. J. R. Statist. Soc. B, 62, 77-87.
29. Tierney, L. (1994) Markov chains for exploring posterior distributions. Ann. Statist., 22, 1701-1762.
30. Vines, S. K., Gilks, W. R. and Wild, P. (1996) Bayesian multiple random effects models. Statist. Comput., 6, 337-346.
31. Williams, D. A. (1987) Reader reaction: dose-response models for teratological experiments. Biometrics, 43, 1013-1016.
32. Zeger, S. L. and Karim, M. R. (1991) Generalized linear models with random effects: a Gibbs sampling approach. J. Am. Statist. Ass., 86, 79-86.