Maximum likelihood estimation for joint mean-covariance models from unbalanced repeated-measures data

DOI10.1016/j.spl.2006.07.013zbMath1106.62025OpenAlexW2068820948MaRDI QIDQ871018

Christine Spinka, Scott H. Holan

Publication date: 15 March 2007

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2006.07.013

zbMATH Keywords

asymptotic normalitymaximum likelihood estimationunbalanced designmodified Cholesky decompositionassociated populationsindependent not identically distributed (i.n.i.d.)

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Point estimation (62F10)

Related Items (3)

Covariate‐based cepstral parameterizations for time‐varying spatial error covariances ⋮ A cautionary note on generalized linear models for covariance of unbalanced longitudinal data ⋮ Robust estimation of the correlation matrix of longitudinal data

Cites Work

• The Matrix-Logarithmic Covariance Model
• The asymptotic properties of ML estimators when sampling from associated populations
• Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Asymptotic Properties of Maximum Likelihood Estimators for the Independent Not Identically Distributed Case
• A NOTE ON THE CONSISTENCY AND MAXIMA OF THE ROOTS OF LIKELIHOOD EQUATIONS
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