文献详情
Joint mean-covariance random effect model for longitudinal data
文献类型：期刊
通讯作者：Tian, MZ (reprint author), Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing 100872, Peoples R China.
期刊名称：BIOMETRICAL JOURNAL
年：2020
卷：62
期：1
页码：7-23
ISSN：0323-3847
关键词：joint mean-covariance model; longitudinal data; MCEM algorithm; random effect
所属部门：统计学院
摘要：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 ...More
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. ...Hide

DOI：10.1002/bimj.201800311
百度学术：Joint mean-covariance random effect model for longitudinal data
语言：外文
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11861042]; Renmin University of China [18XNL012]
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