Maximum Likelihood from Incomplete Data Via the EM Algorithm

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.

Keywords

Maximum likelihoodExpectation–maximization algorithmComputer scienceAlgorithmMathematicsStatistics

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Publication Info

Year: 1977
Type: article
Volume: 39
Issue: 1
Pages: 1-22
Citations: 48916
Access: Closed

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APA Style

                            
                                    A. P. Dempster, 
                                
                                    N. M. Laird, 
                                
                                    Donald B. Rubin
                                
                            (1977). 
                            Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm. 
                            Journal of the Royal Statistical Society Series B (Statistical Methodology)
                            , 39
                            (1)
                            , 1-22.
                            https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

Identifiers

DOI: 10.1111/j.2517-6161.1977.tb01600.x