Center-adjusted inference for a nonparametric bayesian random effect distribution

Statistica Sinica

Volumn 21, Issue 3, 2011, Pages 1201-1223

  Li, Yisheng ^a   Müller, Peter ^b   Lin, Xihong ^c  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Bayesian nonparametric model; Dirichlet process; Fixed effects; Generalized linear mixed model; Post processing; Random moments; Random probability measure

Indexed keywords

EID: 79958262022     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: 10.5705/ss.2009.180     Document Type: Article

References (18)

1. Bernado, J. M. and Smith, A. F. M. (1994). Bayesian Theory. John Wiley and Sons, New York.
2. Bush, C. A. and MacEachern, S. N. (1996). A semiparametric Bayesian model for randomised block designs. Biometrika 83, 275-285.
3. Epifani, I., Guglielmi, A. and Melilli, E. (2006). A stochastic equation for the law of the random Dirichlet variance. Statist. Probab. Lett. 76, 495-502. (Pubitemid 43230424)
4. Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Ann. Statist. 1, 209-230.
5. Gelfand, A. E. and Kottas, A. (2002). A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models. J. Comput. Graph. Statist. 11, 289-305.
6. Gelfand, A. E. and Mukhopadhyay, S. (1995). On nonparametric Bayesian inference for the distribution of a random sample. Canad. J. Statist. 23, 411-420. (Pubitemid 126036815)
7. Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995). Efficient parametrisations for normal linear mixed models. Biometrika 82, 479-488.
8. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In Bayesian Statistics 4 (Edited by J. M. Bernardo, J. Berger, A. P. Dawid and A. F. M. Smith), 169-193. Oxford University Press, Oxford, U.K..
9. Hjort, N. L. and Ongaro, A. (2005). Exact inference for random Dirichlet means. Stat. Inference Stoch. Process. 8, 227-254. (Pubitemid 41261127)
10. Kleinman, K. P. and Ibrahim, J. G. (1998a). A semi-parametric Bayesian approach to generalized linear mixed models. Statist. Medicine 17, 2579-2596. (Pubitemid 28510650)
11. Kleinman, K. P. and Ibrahim, J. G. (1998b). A semiparametric Bayesian approach to the random effects model. Biometrics 54, 921-938. (Pubitemid 28429287)
12. Lijoi, A. and Regazzini, E. (2004). Means of Dirichlet process and hypergeometric functions. Ann. Probab. 32, 1469-1495. (Pubitemid 38845547)
13. Natarajan, R. and Kass, R. E. (2000). Reference Bayesian methods for generalized linear mixed models. J. Amer. Statist. Assoc. 95, 227-237.
14. Natarajan, R. and McCulloch, C. E. (1998). Gibbs sampling with diffuse proper priors: a valid approach to data-driven inference? J. Comput. Graph. Statist. 7, 267-277.
15. Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. J. Comput. Graph. Statist. 9, 249-265.
16. Newton, M. A., Czado, C. and Chappell, R. (1996). Bayesian inference for semiparametric binary regression. J. Amer. Statist. Assoc. 91, 142-153. (Pubitemid 126353374)
17. Verbeke, G. and Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. J. Amer. Statist. Assoc. 433, 217-221. (Pubitemid 126353382)
18. Zhang, S., Müller, P. and Do, K.-A. (2010). A Bayesian semi-parametric survival model with longitudinal markers. Biometrics 66, 435-443.