Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
A. P. DempsterHarvard University and Educational Testing ServiceN. M. LairdHarvard University and Educational Testing ServiceDonald B. RubinHarvard University and Educational Testing Service
1977en
ABI
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.
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