Maximum Likelihood from Incomplete Data via the EM Algorithm.

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1-38.

Type

Journal article

Intellectual base

Primary research

Year

1977

Volume

39

Issue

1

Pages

1-38

ISSN

2517-6161

DOI

10.1111/j.2517-6161.1977.tb01600.x

DOI registrant

Oxford University Press

Link

https://www.maths.usyd.edu.au/u/jchan/GLM/Dempster1977EM.pdf

Cited from

data_science

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Imputation
  • The expectation-maximization algorithm, an imputation-based fitting procedure for incomplete data, transfers across latent-class models, hidden Markov models, and factor analysis, all instantiating the same impute-then-fit cycle.

    This sourceThe expectation-maximization algorithm — an impute-then-fit cycle transferring across latent-class, hidden-Markov, and factor-analysis models.

    Supported in partVerified against the publisher's abstract

    The 1977 abstract documents EM as a broadly applicable maximum-likelihood method for incomplete data, with factor-analysis and finite-mixture examples, but says nothing about hidden Markov models or an impute-then-fit cycle.

    “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, …”

    Read by an automated reader; not a human review. How support was checked

Domain-specific

• Expectation–Maximization Algorithm
  • The expectation is evaluated using the current parameter, while the maximization chooses the next one.

Verification

Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.

Does it back the claim? Read against the text for 1 of 2 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://doi.org/10.1111/j.2517-6161.1977.tb01600.x ×2

Registry ID ref:6ebad94ba46c · see in the full table