GEE analysis in joint mean-covariance model for longitudinal data

被引：1
|
作者：
Lu, Fei [1 ]
Xue, Liugen [1 ]
Cai, Xiong [1 ]
机构：
[1] Beijing Univ Technol, Coll Appl Sci, Beijing, Peoples R China
来源：
STATISTICS & PROBABILITY LETTERS | 2020年 / 160卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Robust estimation; Correlation matrix; Generalized estimating equations; Longitudinal data; Cholesky decomposition; GENERALIZED ESTIMATING EQUATIONS; MATRIX;
D O I：
10.1016/j.spl.2020.108705
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, we propose generalized estimating equations for the regression parameters in joint mean-covariance model for longitudinal data, motivated by the alternative Cholesky decomposition. This decomposition causes robust estimation of the correlation matrix against model misspecification for innovation variances. (C) 2020 Elsevier B.V. All rights reserved.
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页数：7
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