Relating the Classical Covariance Adjustment Techniques of Multivariate Growth Curve Models to Modern Univariate Mixed Effects Models

DOI10.1111/j.0006-341X.1999.00957.xzbMath1059.62678OpenAlexW2051519457WikidataQ43528069 ScholiaQ43528069MaRDI QIDQ4666683

Gary O. Zerbe, Susan K. Mikulich, Thomas J. Crowley, Richard H. Jones

Publication date: 13 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.1999.00957.x

zbMATH Keywords

multivariate growth curve modelmixed modelcovariance adjustmentcosinor analysis

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Response transformations in repeated measures and growth curve models ⋮ Dynamic Conditionally Linear Mixed Models for Longitudinal Data ⋮ Adaptive fitting of linear mixed-effects models with correlated random effects ⋮ A Comparison of REML and Covariance Adjustment Method in the Estimation of Growth Curve Models

Uses Software

• SAS
• SAS/STAT

Cites Work

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• Random-Effects Models for Longitudinal Data
• A note on a Manova model applied to problems in growth curve
• Some Statistical Methods for Comparison of Growth Curves
• The Effect of Covariance Structure on Variance Estimation in Balanced Growth-Curve Models with Random Parameters
• SOME PROBLEMS INVOLVING LINEAR HYPOTHESES IN MULTIVARIATE ANALYSIS
• On Application of Growth Curve Techniques to Time Series Data
• Serial correlation or random subject effects
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm
• A generalized multivariate analysis of variance model useful especially for growth curve problems
• The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves