Working correlation structure selection in generalized estimating equations

被引：7

作者：

Fu, Liya ^{[1
]}

Hao, Yangyang ^{[2
]}

Wang, You-Gan ^{[3
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Shaanxi, Peoples R China

[2] Shanghai Maritime Univ, Logist Res Ctr, Shanghai, Peoples R China

[3] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia

来源：

COMPUTATIONAL STATISTICS | 2018年 / 33卷 / 02期

基金：

澳大利亚研究理事会; 美国国家科学基金会;

关键词：

Correlation information criterion; Empirical likelihood; Longitudinal data; Model selection; LONGITUDINAL DATA; BINARY DATA; MODELS; COVARIATE; CRITERION;

D O I：

10.1007/s00180-018-0800-4

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：

Selecting an appropriate correlation structure in analyzing longitudinal data can greatly improve the efficiency of parameter estimation, which leads to more reliable statistical inference. A number of such criteria have been proposed in the literature from different perspectives. However, little is known about the relative performance of these criteria. We review and evaluate these criteria by carrying out extensive simulation studies. Surprisingly, we find that the AIC and the BIC based on either the Gaussian working likelihood or the empirical likelihood outperform the others.

引用

页码：983 / 996

页数：14

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