Empirical Likelihood for Generalized Linear Models with Longitudinal Data

被引：1

作者：

Yin, Changming ^{[1
]}

Ai, Mingyao ^{[2
,3
]}

Chen, Xia ^{[4
]}

Kong, Xiangshun ^{[5
]}

机构：

[1] Guangxi Univ, Sch Math & Informat Sci, Nanning 530004, Peoples R China

[2] Peking Univ, Sch Math Sci, LMAM, Beijing 100871, Peoples R China

[3] Peking Univ, Ctr Stat Sci, Beijing 100871, Peoples R China

[4] Shaanxi Normal Univ, Sch Math & Stat, Xian 710062, Peoples R China

[5] Beijing Inst Technol, Sch Math & Stat, Beijing 100081, Peoples R China

来源：

JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY | 2023年 / 36卷 / 05期

关键词：

Empirical likelihood ratio; generalized linear model; longitudinal data; maximum empirical likelihood estimator; ESTIMATING EQUATIONS;

D O I：

10.1007/s11424-023-2022-2

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Generalized linear models are usually adopted to model the discrete or nonnegative responses. In this paper, empirical likelihood inference for fixed design generalized linear models with longitudinal data is investigated. Under some mild conditions, the consistency and asymptotic normality of the maximum empirical likelihood estimator are established, and the asymptotic chi(2) distribution of the empirical log-likelihood ratio is also obtained. Compared with the existing results, the new conditions are more weak and easy to verify. Some simulations are presented to illustrate these asymptotic properties.

引用

页码：2100 / 2124

页数：25

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