Maximum likelihood estimation for probit-linear mixed models with correlated random effects

Biometrics

Volumn 53, Issue 1, 1997, Pages 86-97

  Chan, Jennifer S K ^a   Kuk, Anthony Y C ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

EM algorithm; Gibbs sampling; Monte Carlo method; Probit normal models; Random effects; Threshold models

Indexed keywords

APPROXIMATION ALGORITHMS; MONTE CARLO METHODS; RANDOM PROCESSES;

BINARY DATA; EM ALGORITHMS; GIBBS SAMPLING; LINEAR MIXED MODELS; MAXIMUM-LIKELIHOOD ESTIMATION; MONTECARLO METHODS; NORMAL MODEL; PROBIT-NORMAL MODEL; RANDOM EFFECTS; THRESHOLD MODEL;

MAXIMUM LIKELIHOOD ESTIMATION;

ALGORITHM; ARTICLE; ERROR; RANDOMIZATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; SYSTEM ANALYSIS;

AMPHIUMA MEANS; ANIMALIA;

EID: 0030933587     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/2533099     Document Type: Article

References (19)

1. Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9-25.
2. Drum, M. L. and McCullagh, P. (1993). REML estimation with exact covariance in the logistic mixed model. Biometrics 49, 677-689.
3. Foulley, J. L., Gianola, D., and Im, S. (1990). Genetic evaluation for discrete polygenic traits in animal breeding. In Advances in Statistical Methods for Genetic Improvement of Livestock, D. Gianola and K. Hammond (eds), 361-411. New York: Springer-Verlag.
4. Karim, M. R. and Zeger, S. L. (1992). Generalized linear models with random effects; salamander data revisited. Biometrics 48, 631-644.
5. Kuk, A. Y. C. (1995). Asymptotically unbiased estimation in generalised linear models with random effects. Journal of the Royal Statistical Society, Series B 57, 395-407.
6. Kuk, A. Y. C. and Chen, C. H. (1992). A mixture model combining logistic regression with proportional hazards regression. Biometrika 79, 531-541.
7. Liu, C. and Rubin, D. B. (1994). The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence. Biometrika 81, 633-648.
8. Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 226-233.
9. Marsaglia, G. (1964). Generating a variable from the tail of the normal distribution. Technometrics 6, 101-102.
10. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Model, 2nd edition. London: Chapman and Hall.
11. McCulloch, C. E. (1994). Maximum likelihood variance components estimation for binary data. Journal of the American Statistical Association 89, 330-335.
12. McGilchrist, C. A. (1994). Estimation in generalized mixed models. Journal of the Royal Statistical Society, Series B 56, 61-69.
13. Meng, X. L. and Rubin, D. B. (1991). Using EM to obtain asymptotic variance-covariance matrices: The SEM algorithm. Journal of the American Statistical Association 86, 899-909.
14. Schall, R. (1991). Estimation in generalised linear models with random effects. Biometrika 78, 719-727.
15. Smith, A. F. M. and Roberts, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B 55, 3-23.
16. Waclawiw, M. A. and Liang, K. Y. (1993). Prediction of random effects in the generalized linear model. Journal of the American Statistical Association 88, 171-178.
17. Wei, G. C. G. and Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 85, 699-704.
18. Zeger, S. L. and Karim, M. R. (1991). Generalized linear models with random effects: A Gibbs sampling approach. Journal of the American Statistical Association 86, 79-86.
19. Zeger, S. L., Liang, K. Y., and Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics 44, 1049-1060.