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Joint mean–covariance random effect model for longitudinal data
Author(s) -
Bai Yongxin,
Qian Manling,
Tian Maozai
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201800311
Subject(s) - covariance , cholesky decomposition , covariance intersection , estimator , covariance mapping , mathematics , statistics , covariance function , estimation of covariance matrices , random effects model , computer science , meta analysis , medicine , eigenvalues and eigenvectors , physics , quantum mechanics
In this paper, we consider the inherent association between mean and covariance in the joint mean–covariance modeling and propose a joint mean–covariance random effect model based on the modified Cholesky decomposition for longitudinal data. Meanwhile, we apply M‐H algorithm to simulate the posterior distributions of model parameters. Besides, a computationally efficient Monte Carlo expectation maximization (MCEM) algorithm is developed for carrying out maximum likelihood estimation. Simulation studies show that the model taking into account the inherent association between mean and covariance has smaller standard deviations of the estimators of parameters, which makes the statistical inferences much more reliable. In the real data analysis, the estimation of parameters in the mean and covariance structure is highly efficient.