Regressograms and Mean-Covariance Models for Incomplete Longitudinal Data

DOI10.1080/00031305.2012.695935OpenAlexW2046937456WikidataQ58150925 ScholiaQ58150925MaRDI QIDQ5876895

Tanya P. Garcia, Priya Kohli, Mohsen Pourahmadi

Publication date: 2 February 2023

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2012.695935

zbMATH Keywords

missing datacovariance structureexpectation-maximizationgraphical toolsregressograms

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Optimal designs for mean-covariance models with missing observations ⋮ Robust estimation of mean and covariance for longitudinal data with dropouts ⋮ Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data

Cites Work

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• Computing the nearest correlation matrix--a problem from finance
• A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
• A study of algorithms for covariance structure analysis with specific comparisons using factor analysis
• Estimation of multivariate normal covariance and precision matrices in a star-shape model with missing data
• Inference and missing data
• Direct Calculation of the Information Matrix via the EM Algorithm
• On modelling mean-covariance structures in longitudinal studies
• Graphical Diagnostics for Modeling Unstructured Covariance Matrices
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation

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