-
1
-
-
0002241694
-
The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problems
-
Celuex G., and Diebolt J. The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problems. Computat. Statist. Quart. 2 (1985) 73-82
-
(1985)
Computat. Statist. Quart.
, vol.2
, pp. 73-82
-
-
Celuex, G.1
Diebolt, J.2
-
3
-
-
0033243858
-
Convergence of a stochastic approximation version of the EM algorithm
-
Delyon B., Lavielle M., and Moulines E. Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27 (1999) 94-128
-
(1999)
Ann. Statist.
, vol.27
, pp. 94-128
-
-
Delyon, B.1
Lavielle, M.2
Moulines, E.3
-
4
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm (with discussion)
-
Dempster A.P., Larid N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. B 39 (1977) 1-38
-
(1977)
J. Roy. Statist. Soc. B
, vol.39
, pp. 1-38
-
-
Dempster, A.P.1
Larid, N.M.2
Rubin, D.B.3
-
5
-
-
0002241603
-
Stochastic EM: method and application
-
Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds), Chapman & Hall, London (Chapter 15)
-
Diebolt J., and Ip E.H.S. Stochastic EM: method and application. In: Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall, London (Chapter 15)
-
(1996)
Markov Chain Monte Carlo in Practice
-
-
Diebolt, J.1
Ip, E.H.S.2
-
6
-
-
4243828610
-
Informative dropout in longitudinal data analysis
-
Diggle P.J., and Kenward M.G. Informative dropout in longitudinal data analysis. J. Roy. Statist. Soc. B 43 (1994) 49-93
-
(1994)
J. Roy. Statist. Soc. B
, vol.43
, pp. 49-93
-
-
Diggle, P.J.1
Kenward, M.G.2
-
7
-
-
21344497888
-
Missing data, imputation, and the bootstrap
-
Efron B. Missing data, imputation, and the bootstrap. J. Amer. Statist. Assoc. 89 (1994) 463-475
-
(1994)
J. Amer. Statist. Assoc.
, vol.89
, pp. 463-475
-
-
Efron, B.1
-
9
-
-
84972492387
-
Inference from iterative simulation using multiple sequences (with discussion)
-
Gelman A., and Rubin D.B. Inference from iterative simulation using multiple sequences (with discussion). Statist. Sci. 7 (1992) 457-511
-
(1992)
Statist. Sci.
, vol.7
, pp. 457-511
-
-
Gelman, A.1
Rubin, D.B.2
-
10
-
-
13144294075
-
A stochastic approximation algorithm with Markov chain Monte Carlo method for incomplete data estimation problems
-
Gu M.G., and Kong F.H. A stochastic approximation algorithm with Markov chain Monte Carlo method for incomplete data estimation problems. Proc. Natl. Acad. Sci. USA 98 (1998) 7270-7274
-
(1998)
Proc. Natl. Acad. Sci. USA
, vol.98
, pp. 7270-7274
-
-
Gu, M.G.1
Kong, F.H.2
-
11
-
-
0026537001
-
Quality of life measures for patients receiving adjutant therapy for breast cancer: an international trial
-
Hürny C., Bernhard J., Gelber R.D., Coates A., Gastiglione M., Isley M., Dreher D., peterson H., Goldhirsch A., and Senn H.J. Quality of life measures for patients receiving adjutant therapy for breast cancer: an international trial. European J. Cancer 28 (1992) 118-124
-
(1992)
European J. Cancer
, vol.28
, pp. 118-124
-
-
Hürny, C.1
Bernhard, J.2
Gelber, R.D.3
Coates, A.4
Gastiglione, M.5
Isley, M.6
Dreher, D.7
peterson, H.8
Goldhirsch, A.9
Senn, H.J.10
-
12
-
-
0037507563
-
Missing responses in generalized linear mixed models when the missing data mechanism is nonignorable
-
Ibrahim J.G., Chen M.H., and Lipsitz S.R. Missing responses in generalized linear mixed models when the missing data mechanism is nonignorable. Biometrika 88 (2001) 551-564
-
(2001)
Biometrika
, vol.88
, pp. 551-564
-
-
Ibrahim, J.G.1
Chen, M.H.2
Lipsitz, S.R.3
-
13
-
-
33646106277
-
-
Ip, E.H.S., 1994. A stochastic EM estimator in the presence of missing data: theory and applications. Technical Report, Division of Biostatistics, Stanford University, Stanford, California, US.
-
-
-
-
14
-
-
0022966316
-
Unbalanced repeated measures models with structured covariance matrices
-
Jennrich R.I., and Schluchter M.D. Unbalanced repeated measures models with structured covariance matrices. Biometrika 42 (1986) 805-820
-
(1986)
Biometrika
, vol.42
, pp. 805-820
-
-
Jennrich, R.I.1
Schluchter, M.D.2
-
15
-
-
0000808747
-
A gradient algorithm locally equivalent to the EM algorithm
-
Lange K.L. A gradient algorithm locally equivalent to the EM algorithm. J. Roy. Statist. Soc. B 57 (1995) 425-437
-
(1995)
J. Roy. Statist. Soc. B
, vol.57
, pp. 425-437
-
-
Lange, K.L.1
-
17
-
-
0000315742
-
The ECME algorithm: a simple extension of EM and ECM with faster monotone convergence
-
Liu C., and Rubin D.B. The ECME algorithm: a simple extension of EM and ECM with faster monotone convergence. Biometrika 81 (1994) 633-648
-
(1994)
Biometrika
, vol.81
, pp. 633-648
-
-
Liu, C.1
Rubin, D.B.2
-
18
-
-
0001044972
-
Finding the observed information matrix when using the EM algorithm
-
Louis T.A. Finding the observed information matrix when using the EM algorithm. J. Roy. Statist. Soc. B 44 (1982) 226-232
-
(1982)
J. Roy. Statist. Soc. B
, vol.44
, pp. 226-232
-
-
Louis, T.A.1
-
20
-
-
0001232538
-
A fast improvement to the EM algorithm on its own terms
-
Meilijson I. A fast improvement to the EM algorithm on its own terms. J. Roy. Statist. Soc. B 51 (1989) 127-138
-
(1989)
J. Roy. Statist. Soc. B
, vol.51
, pp. 127-138
-
-
Meilijson, I.1
-
21
-
-
84864615423
-
Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm
-
Meng X.L., and Rubin D.B. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. J. Amer. Statist. Assoc. 86 (1991) 899-909
-
(1991)
J. Amer. Statist. Assoc.
, vol.86
, pp. 899-909
-
-
Meng, X.L.1
Rubin, D.B.2
-
22
-
-
0000251971
-
Maximum likelihood estimation via the ECM algorithm: a general framework
-
Meng X.L., and Rubin D.B. Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika 80 (1993) 267-278
-
(1993)
Biometrika
, vol.80
, pp. 267-278
-
-
Meng, X.L.1
Rubin, D.B.2
-
23
-
-
33646075661
-
-
Meng, X.L., van Dyk, D., 1995. The EM algorithm-an old folk song sung to a fast new tune. Technical Report No. 408. Department of Statistics, University of Chicago, Chicago.
-
-
-
-
24
-
-
0000238336
-
A simplex method for function minimisation
-
Nelder J.A., and Mead R. A simplex method for function minimisation. Comput. J. 7 (1965) 303-313
-
(1965)
Comput. J.
, vol.7
, pp. 303-313
-
-
Nelder, J.A.1
Mead, R.2
-
25
-
-
0027248708
-
Improving the EM algorithm
-
Rai S.N., and Matthews D.E. Improving the EM algorithm. Biometrics 49 (1993) 587-591
-
(1993)
Biometrics
, vol.49
, pp. 587-591
-
-
Rai, S.N.1
Matthews, D.E.2
-
26
-
-
0017133178
-
Inference and missing data
-
Rubin D.B. Inference and missing data. Biometrika 63 (1976) 581-592
-
(1976)
Biometrika
, vol.63
, pp. 581-592
-
-
Rubin, D.B.1
-
27
-
-
84950758368
-
The calculation of posterior distributions by data augmentation (with discussion)
-
Tanner M.A., and Wong W.H. The calculation of posterior distributions by data augmentation (with discussion). J. Amer. Statist. Assoc. 82 (1987) 528-550
-
(1987)
J. Amer. Statist. Assoc.
, vol.82
, pp. 528-550
-
-
Tanner, M.A.1
Wong, W.H.2
-
28
-
-
0039982650
-
Analysis of longitudinal data with non-ignorable non monotone missing values
-
Troxel A.B., Harrington D.P., and Lipsitz S.R. Analysis of longitudinal data with non-ignorable non monotone missing values. Appl. Statist. 47 (1998) 425-438
-
(1998)
Appl. Statist.
, vol.47
, pp. 425-438
-
-
Troxel, A.B.1
Harrington, D.P.2
Lipsitz, S.R.3
-
29
-
-
33646100934
-
A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithm
-
Wei G.C.G., and Tanner M.A. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithm. J. Roy. Statist. Soc. B 55 (1990) 425-437
-
(1990)
J. Roy. Statist. Soc. B
, vol.55
, pp. 425-437
-
-
Wei, G.C.G.1
Tanner, M.A.2
-
30
-
-
0141791115
-
Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte Carlo method
-
Zhu H.T., and Lee S.Y. Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte Carlo method. Statist. Comput. 12 (2002) 175-183
-
(2002)
Statist. Comput.
, vol.12
, pp. 175-183
-
-
Zhu, H.T.1
Lee, S.Y.2
|