A Comparison of REML and Covariance Adjustment Method in the Estimation of Growth Curve Models

DOI10.1080/03610920802162649zbMath1292.62106OpenAlexW2044863661MaRDI QIDQ5495070

Vassilis G. S. Vasdekis

Publication date: 30 July 2014

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

Full work available at URL: https://doi.org/10.1080/03610920802162649

zbMATH Keywords

longitudinal dataREMLgrowth curvescovariance adjustment

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (2)

Rank Preserved Aggregation Rules and Application to Reliability Allocation ⋮ Unnamed Item

Cites Work

• Optimal covariance adjustment in growth curve models
• Form and selection of covariance adjusted estimators in repeated measures models
• A note on a Manova model applied to problems in growth curve
• Nonparametric estimation of large covariance matrices of longitudinal data
• Unbalanced Repeated-Measures Models with Structured Covariance Matrices
• An Approach to the Analysis of Repeated Measurements
• Selection of covariables in the growth curve model
• REML Estimation with Exact Covariance in the Logistic Mixed Model
• Modified AIC and Cp in multivariate linear regression
• Bayesian analysis of covariance matrices and dynamic models for longitudinal data
• Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
• Relating the Classical Covariance Adjustment Techniques of Multivariate Growth Curve Models to Modern Univariate Mixed Effects Models
• Model Selection for Multivariate Regression in Small Samples
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Recovery of inter-block information when block sizes are unequal

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