Estimation of the mean when data contain non-ignorable missing values from a random effects model

DOI10.1016/0167-7152(94)90112-0zbMath0795.62020OpenAlexW1989367184MaRDI QIDQ1324611

Myron N. Chang, Hui Quan, Weichung Joseph Shih

Publication date: 24 May 1994

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(94)90112-0

zbMATH Keywords

EM algorithmasymptotic biasmaximum likelihoodmissing at randomsample meanrandom effects modelmissing valuesECM algorithmmoment estimatorsmarginal meandistinct parametersinference of incomplete datanon-ignorable missing data process

Mathematics Subject Classification ID

Point estimation (62F10)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Random-Effects Models for Longitudinal Data
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Empirical Bayes Estimation of Rates in Longitudinal Studies
• The effect of measurement error
• Inference and missing data

This page was built for publication: Estimation of the mean when data contain non-ignorable missing values from a random effects model