Maximum Likelihood from Incomplete Data Via the EM Algorithm

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ID: 289058
1977
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Ranked #105 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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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.
Reference Key
openalex_W2049633694 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors A. P. Dempster, N. M. Laird, Donald B. Rubin
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1977
DOI
10.1111/j.2517-6161.1977.tb01600.x
URL
https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Keywords Keywords not found

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