articleTop 1% cited
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1977 · Vol. 39(1) · pp. 1–22
A. P. Dempster✉(Educational Testing Service)N. M. Laird(Harvard University)Donald B. Rubin(Harvard University)
Abstract
Summary 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.
Control Systems and IdentificationStatistical Methods and InferenceGaussian Processes and Bayesian InferenceMaximum likelihoodExpectation–maximization algorithmComputer scienceAlgorithmMathematicsStatistics
Funding
- National Science Foundation
Citations
49,286
FWCI
63.90
field-weighted impact
References
84
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100%
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References
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Inference and missing data
Biometrika · 1976 · 9,558 citations
Exploratory latent structure analysis using both identifiable and unidentifiable models
Biometrika · 1974 · 1,597 citations
The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1976 · 1,793 citations
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