A multiple-imputation Metropolis version of the EM algorithm

Biometrika

Volumn 90, Issue 3, 2003, Pages 643-654

  Gaetan, Carlo ^a   Yao, Jian Feng ^b  

^a UNIVERSITY OF PADOVA   (Italy)

^b UNIVERSITÉ DE RENNES 1   (France)

Author keywords

Incomplete data; Mixed model; Multiple imputation; Simulated annealing; Stochastic EM

Indexed keywords

EID: 3843106737     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/90.3.643     Document Type: Article

References (18)

1. ARSLAN, O., CONSTABLE, P. D. L. & KENT, J. T. (1993). Domains of convergence for the EM algorithm: a cautionary tale in a location estimation problem. Statist. Comp. 3, 103-8.
2. BOOTH, J. G. & HOBERT, J. P. (1999). Maximizing generalized linear model likelihoods with an automated Monte Carlo EM algorithm. J. R. Statist. Soc. B 61, 265-85.
3. CELEUX, G. & DIEBOLT, J. (1985). The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Comp. Statist. Quart. 2, 73-82.
4. CELEUX, G. & DIEBOLT, J. (1993). Asymptotic properties of the stochastic EM algorithm for estimating mixing proportions. Commun. Statist. Stock. Models 9, 599-613.
5. CHADOEUF, J., SENOUSSI, R. & YAO, J. F. (2000). Parametric estimation of a Boolean segment process with stochastic restoration estimation. J. Comp. Graph. Statist. 9, 390-402.
6. DELYON, B., LAVIELLE, M. & MOULINES, E. (1999). Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27, 94-128.
7. DEMPSTER, A. P., LAIRD, N. M. & RUBIN, D. B. (1977). Maximum-likelihood from incomplete data via the EM algorithm (with Discussion). J. R. Statist. Soc. B 39, 1-38.
8. DOUCET, A., GODSILL, S. J. & ROBERT, C. P. (2002). Marginal maximum a posterior estimation using Markov chain Monte Carlo. Statist. Comp. 12, 77-84.
9. HAARIO, H. & SACKSMAN, E. (1991). Simulated annealing process in general state space. Adv. Appl. Prob. 23, 866-93.
10. LEVINE, R. A. & CASELLA, G. (2000). Implementations of the Monte Carlo EM algorithm. J. Comp. Graph. Statist. 10, 422-39.
11. MCCULLOCH, C. E. (1997). Maximum likelihood algorithms for generalized linear mixed models. J. Am. Statist. Assoc. 92, 162-70.
12. MENG, X. & VAN DYK, D. (1997). The EM algorithm - an old folk-song sung to a fast new tune (with Discussion). J. R. Statist. Soc. B 59, 511-67.
13. NIELSEN, S. F. (2000). The stochastic EM algorithm: estimation and asymptotic results. Bernoulli 6, 457-89.
14. RUBIN, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
15. TANNER, M. A. & WONG, W. H. (1987). The calculation of posterior distributions by data augmentation (with Discussion). J. Am. Statist. Assoc. 82, 528-50.
16. VAN LAARHOVEN, P. J. M. & AARTS, E. H. L. (1987). Simulated Annealing: Theory and Applications. Dordrecht: Kluwer Academic Publishers.
17. WEI, G. C. G. & TANNER, M. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation. J. Am. Statist. Assoc. 85, 699-704.
18. YAO, J. (2000). On recursive estimation in incomplete data models. Statistics 34, 27-51.