Effect of Violation of the Normal Assumption on MI and ML Estimators in the Analysis of Incomplete Data

DOI10.1080/03610926.2013.819920zbMath1332.62080OpenAlexW2010968162MaRDI QIDQ2794791

Michio Yamamoto, Shintaro Hojo, Yutaka Kano

Publication date: 11 March 2016

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

Full work available at URL: https://doi.org/10.1080/03610926.2013.819920

zbMATH Keywords

incomplete datamaximum likelihoodmultiple imputationdistribution misspecification

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05)

Cites Work

• Unnamed Item
• Normal distribution based pseudo ML for missing data: with applications to mean and covariance structure analysis
• Asymptotic results for multiple imputation
• Consistency of Normal-Distribution-Based Pseudo Maximum Likelihood Estimates When Data Are Missing at Random
• Inference and missing data
• Large-sample theory for parametric multiple imputation procedures
• Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment

This page was built for publication: Effect of Violation of the Normal Assumption on MI and ML Estimators in the Analysis of Incomplete Data