Generalized empirical likelihood inference in partial linear regression model for longitudinal data

被引:3
|
作者
Tian, Ruiqin [1 ,2 ]
Xue, Liugen [2 ]
机构
[1] Zhejiang Agr & Forestry Univ, Dept Stat, Hangzhou, Zhejiang, Peoples R China
[2] Beijing Univ Technol, Coll Appl Sci, Beijing, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Longitudinal data; empirical likelihood; confidence region; maximum empirical likelihood estimator; partial linear model; 62G05; 62G20; VARYING-COEFFICIENT MODEL; SINGLE-INDEX MODEL; CONFIDENCE-REGIONS; ESTIMATING EQUATIONS; MOMENTS;
D O I
10.1080/02331888.2017.1355370
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, empirical likelihood inference for longitudinal data within the framework of partial linear regression models are investigated. The proposed procedures take into consideration the correlation within groups without involving direct estimation of nuisance parameters in the correlation matrix. The empirical likelihood method is used to estimate the regression coefficients and the baseline function, and to construct confidence intervals. A nonparametric version of Wilk's theorem for the limiting distribution of the empirical likelihood ratio is derived. Compared with methods based on normal approximations, the empirical likelihood does not require consistent estimators for the asymptotic variance and bias. The finite sample behaviour of the proposed method is evaluated with simulation and illustrated with an AIDS clinical trial data set.
引用
收藏
页码:988 / 1005
页数:18
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