Model comparison of generalized linear mixed models

Statistics in Medicine

Volumn 25, Issue 10, 2006, Pages 1685-1698

  Song, Xin Yuan ^a   Lee, Sik Yum ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bayesian information criterion; Latent random effects; Maximum likehood estimation; Observed data log likelihood; Path sampling

Indexed keywords

AIR POLLUTION; ARTICLE; BAYES THEOREM; CIGARETTE SMOKING; COMPUTER ANALYSIS; HEALTH HAZARD; HUMAN; HYPOTHESIS; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LINEAR SYSTEM; LONGITUDINAL STUDY; LUNG CANCER; MEDICAL INFORMATION; MEDICAL RESEARCH; METHODOLOGY; RESPIRATORY TRACT INFECTION; SAMPLING; SMOKING HABIT; STATISTICAL MODEL;

AIR POLLUTION; BAYES THEOREM; CHILD; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; MODELS, THEORETICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; OHIO; RESPIRATORY TRACT INFECTIONS; TOBACCO SMOKE POLLUTION;

EID: 33646797704     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2318     Document Type: Article

References (31)

1. Pawitan Y, Reilly M, Nilsson E, Cnattinguis S. Estimation of genetic and environmental factors for binary traits using family data. Statistics in Medicine 2004; 23:449-465.
2. Kleinman K, Lazarus R, Platt R. A generalized linear mixed model approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. American Journal of Epidemiology 2004; 159:217-224.
3. Manton KG, Woodbury MA, Stallard E, Riggan WB, Creason JP, Pellom AI. Empirical Bayes procedure for stabilizing maps of U.S. cancer mortality rates. Journal of the American Statistical Association 1989; 84: 637-650.
4. Zeger SL, Liang KY, Albert YS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44:1049-1060.
5. Zeger SL, Karim MR. Generalized linear model with random effects: a Gibbs sampling approach. Journal of the American Statistical Association 1991; 86:79-86.
6. Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 1993; 88:9-25.
7. Lin X, Breslow NE. Bias correction in generalized linear mixed models with multiple components of dispersion. Journal of the American Statistical Association 1996; 91:1007-1016.
8. Wolfinger R, O'Connell M. Generalized linear models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 1993; 48:233-234.
9. Booth JG, Hobert JP. Maximum generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B 1999; 61:265-285.
10. Zhu HT, Lee SY. Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov Chain Monte Carlo method. Statistical Computing 2002; 12:175-183.
11. Sarton N, Severini TA. Conditional likelihood inference in generalized linear mixed models. Statistica Sinica 2004; 14:349-360.
12. Zhu HT, Lee SY. Local influence for generalized linear mixed models. Canadian Journal of Statistics 2003; 31:293-309.
13. Gelman A, Meng XL. Simulating normalizing constant: from importance sampling to bridge sampling to path sampling. Statistical Science 1998; 13:163-185.
14. Meng XL, Wong WH. Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statistica Sinica 1996; 6:831-860.
15. Lee SY, Song XY. Maximum likelihood analysis of a general latent variable model with hierarchically mixed data. Biometrics 2004; 60:624-636.
16. Lee SY, Song XY. Model comparison of nonlinear structural equation models with fixed covariates. Psychometrika 2003; 68:27-47.
17. McCulloch CE. Maximum likelihood algorithm for generalized linear mixed models. Journal of the American Statistical Association 1997; 92:162-170.
18. Kass RE, Raftery AE. Bayes factors. Journal of the American Statistical Association 1995; 90:773-795.
19. Glonke GFV, McCullagh P. Multivariate logistic models. Journal of the Royal Statistical Society, Series B 1995; 57:533-546.
20. Chib S, Greenberg E. Analysis of multivariate probit models. Biometrika 1998; 85:347-361.
21. Chen JL, Zhang DW, Davidiad M. A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribution. Biostatistics 2002; 3:347-360.
22. Dorn HF. The relationship of cancer of the lung and the use of tobacco. American Statistician 1954; 8:7-13.
23. Cox DR, Snell EJ. Analysis of Binary Data (2nd edn). Chapman & Hall: London, 1989.
24. Tanner MA, Wong WH. The calculation of posterior distribution by data augmentation (with discussion). Journal of the American Statistical Association 1987; 86:79-86.
25. Rubin DB. EM and beyond. Psychometrika 1991; 56:241-254.
26. Little RJA, Rubin DB. Statistical Analysis with Missing Data. Wiley: New York, 2002.
27. Meng XL, Schilling S. Fitting full-information item factor models and an empirical application of bridge sampling. Journal of the American Statistical Association 1996; 91:1254-1267.
28. Little RC, Millcken GA, Stroap WW, Wolfinger RD. SAS System for Mixed Models. SAS Institute: Cary, NC, 1996.
29. Payne RW et al. GenStat Release 8 Reference Manual. VSN Internation: Oxford, U.K., 2005.
30. Louis TA. Finding the observed information matrix when using EM algorithm. Journal of the Royal Statistical Society, Series B 1982; 44:226-233.
31. Chib S, Jeliazkov I. Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association 2001; 96:270-281.