Graphical analysis of residuals in multivariate growth curve models and applications in the analysis of longitudinal data

DOI10.1080/03610918.2020.1775849OpenAlexW3043148834MaRDI QIDQ5042147

Weiliang Huang, Dietrich von Rosen, Jemila S. Hamid

Publication date: 18 October 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2020.1775849

zbMATH Keywords

longitudinal datagrowth curve modeltransformationmodel diagnosticsCholesky decompositionmodel assumptionsdecomposed residualsdecomposition of linear spacesordinary residualsSmall's graphical method

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• A novel trace test for the mean parameters in a multivariate growth curve model
• Maximum likelihood estimators in multivariate linear normal models
• Prediction of future observations in growth curve models. With discussion and a reply by the author
• Bilinear regression analysis. An introduction
• Estimation of the parameters of the extended growth curve model under multivariate skew normal distribution
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• Residuals in the growth curve model
• Inference in the growth curve model under multivariate skew normal distribution
• A note on a Manova model applied to problems in growth curve
• Randomization Analysis of the Completely Randomized Design Extended to Growth and Response Curves
• Tests and Model Selection for the General Growth Curve Model
• Residuals in the Extended Growth Curve Model
• Prediction and Estimation of Growth Curves With Special Covariance Structures
• Detection of Influential Observation in Linear Regression
• Selection of a Covariance Structure for Growth Curves
• Bayesian growth curve model useful for high-dimensional longitudinal data
• A generalized multivariate analysis of variance model useful especially for growth curve problems
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