(Q43501699)

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Maximum Likelihood from Incomplete Data via the EM Algorithm

scientific article published in 1977

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title

Maximum Likelihood from Incomplete Data via the EM Algorithm (English)

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main subject

expectation–maximization algorithm

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1 reference

1

A. P. Dempster

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Nan Laird

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object named as

N. M. Laird

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Donald B. Rubin

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3

object named as

D. B. Rubin

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language of work or name

English

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publication date

1977

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number of pages

38

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published in

Journal of the Royal Statistical Society Series B: Statistical Methodology

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volume

39

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issue

1

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page(s)

1-38

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cites work

A contingency table with missing data

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Some Bayesian stratified two-phase sampling results

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Estimation of the Branch Points of a Branching Diffusion Process

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Theory of Statistical Estimation

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Most plausible estimators true to expectations from triangular truncated contingency tables

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The Analysis of Cross-Classified Data: Independence, Quasi-Independence, and Interactions in Contingency Tables with or without Missing Entries

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Exploratory latent structure analysis using both identifiable and unidentifiable models

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A Remark on the Optimal Regression Designs with Previous Observations of Covey-Crump and Silvey

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Log-Linear Models for Frequency Tables Derived by Indirect Observation: Maximum Likelihood Equations

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Nonorthogonal Analysis of Variance Using Iterative Improvement and Balanced Residuals

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Computing Maximum Likelihood Estimates for the Mixed A.O.V. Model Using the W Transformation

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Improved Algorithm for the W-Transform in Variance Component Estimation

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Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis

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A comparison of iterative methods for obtaining maximum likelihood estimates in contingency tables with a missing diagonal

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Block Designs and Missing Data

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Iterative Procedures for Missing Values in Experiments

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Estimation and tests of significance in factor analysis

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A Non-iterative Algorithm for Least Squares Estimation of Missing Values in any Analysis of Variance Design

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A nonidentifiability aspect of the problem of competing risks

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The Future of Data Analysis

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The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data

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An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology

1 reference

19 June 2022

https://opencitations.net/index/coci/api/v1/citations/10.1090/S0002-9904-1967-11751-8

Identifiers

DOI

10.1111/J.2517-6161.1977.TB01600.X

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JSTOR article ID

2984875

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(Q43501699)

Maximum Likelihood from Incomplete Data via the EM Algorithm

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