On the use of the selection matrix in the maximum likelihood estimation of normal distribution models with missing data

DOI10.1080/03610926.2017.1353631OpenAlexW2738759269MaRDI QIDQ5160263

Keiji Takai

Publication date: 28 October 2021

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2017.1353631

zbMATH Keywords

information matrixEM algorithmnormal distributionmaximum likelihood estimatormissing dataselection matrix

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12)

Cites Work

• What is meant by ``missing at random?
• Normal distribution based pseudo ML for missing data: with applications to mean and covariance structure analysis
• The information matrix of a sample of observations with missing data from a mutlivariate normal distribution with a covariance structure
• Likelihood based frequentist inference when data are missing at random
• Asymptotic Inference with Incomplete Data
• Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
• Maximum likelihood estimation for multivariate normal distribution with monotone sample
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
• Every Missingness not at Random Model Has a Missingness at Random Counterpart with Equal Fit
• A Tutorial on the SWEEP Operator
• Pattern-Mixture Models for Multivariate Incomplete Data
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