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.
%0 Journal Article
%1 Dempster1977
%A Dempster, A P
%A Laird, N M
%A Rubin, D B
%D 1977
%J Journal of the Royal Statistical Society. Series B (Methodological)
%K k-means, unsupervised,clustering
%N 1
%P 1--38
%R 10.2307/2984875
%T Maximum Likelihood from Incomplete Data via the EM Algorithm
%U http://dx.doi.org/10.2307/2984875
%V 39
%X 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.
@article{Dempster1977,
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.},
added-at = {2013-09-29T14:16:50.000+0200},
author = {Dempster, A P and Laird, N M and Rubin, D B},
biburl = {https://www.bibsonomy.org/bibtex/2e3e58b5de7738409f46facf91ef38cce/guillem.palou},
doi = {10.2307/2984875},
interhash = {6a3c3e7e36b05f7855a57eab65f93593},
intrahash = {e3e58b5de7738409f46facf91ef38cce},
issn = {00359246},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
keywords = {k-means, unsupervised,clustering},
number = 1,
pages = {1--38},
timestamp = {2013-09-29T14:16:50.000+0200},
title = {{Maximum Likelihood from Incomplete Data via the EM Algorithm}},
url = {http://dx.doi.org/10.2307/2984875},
volume = 39,
year = 1977
}