Random covariances and mixed-effects models for imputing multivariate multilevel continuous data

DOI10.1177/1471082X1001100404zbMath1420.62279OpenAlexW2076261949WikidataQ34136853 ScholiaQ34136853MaRDI QIDQ5194718

Recai M. Yucel

Publication date: 17 September 2019

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/1471082x1001100404

zbMATH Keywords

missing datamultiple imputationmixed effectslinear mixed-effects modelscomplex sample surveysrandom covariances

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to social sciences (62P25)

Related Items (2)

Missing data: A statistical framework for practice ⋮ Multiple imputation for multilevel data with continuous and binary variables

Uses Software

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• MLwiN
• SAS
• MICE
• BayesDA
• MEMSS
• SAS/STAT

Cites Work

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• Random-Effects Models for Longitudinal Data
• Simultaneous modelling of the Cholesky decomposition of several covariance matrices
• Markov chains for exploring posterior distributions. (With discussion)
• Formalizing Subjective Notions About the Effect of Nonrespondents in Sample Surveys
• Mixed-Effects Models in S and S-PLUS
• Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies
• Mixed Models
• Multilevel models with multivariate mixed response types
• A Potential for Bias When Rounding in Multiple Imputation
• Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment
• Linear mixed models for longitudinal data

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