Maximum Likelihood from Incomplete Data via the EM Algorithm

by: AP Dempster, NM Laird, DB Rubin

(1977)

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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.

@article{dempster1977:convergence_em, 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.}, author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B. }, citeulike-article-id = {117535}, doi = {10.2307/2984875}, keywords = {algorithm, convergence, em, expectation, maximization}, posted-at = {2008-03-17 13:49:37}, priority = {2}, title = {Maximum Likelihood from Incomplete Data via the EM Algorithm}, url = {http://dx.doi.org/10.2307/2984875}, year = {1977} }

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TY - JOUR ID - dempster1977:convergence_em L3 - citeulike-article-id:117535 TI - Maximum Likelihood from Incomplete Data via the EM Algorithm N2 - 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. KW - algorithm KW - convergence KW - em KW - expectation KW - maximization AU - Dempster, AP AU - Laird, NM AU - Rubin, DB PY - 1977/// UR - http://dx.doi.org/10.2307/2984875 ER -

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