If you need an accessible version of this item please contact JSTOR User Support
Maximum Likelihood from Incomplete Data via the EM Algorithm
A. P. Dempster, N. M. Laird and D. B. Rubin
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 39, No. 1 (1977), pp. 1-38
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2984875
Page Count: 38
You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
If you need an accessible version of this item please contact JSTOR User Support
Abstract
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Page Thumbnails
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
-
10
-
11
-
12
-
13
-
14
-
15
-
16
-
17
-
18
-
19
-
20
-
21
-
22
-
23
-
24
-
25
-
26
-
27
-
28
-
29
-
30
-
31
-
32
-
33
-
34
-
35
-
36
-
37
-
38
Journal of the Royal Statistical Society. Series B (Methodological) © 1977 Royal Statistical Society