Semiparametric regression estimation for longitudinal data in models with martingale difference error's structure

被引:3
|
作者
Zhou, Xing-Cai [1 ,2 ]
Lin, Jin-Guan [1 ]
机构
[1] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[2] Tongling Univ, Dept Math & Comp Sci, Tongling 244000, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
semiparametric model; partially linear regression model; longitudinal data; martingale difference; strong consistency; PARTIAL LINEAR-MODELS; ASYMPTOTIC THEORY;
D O I
10.1080/02331888.2011.617819
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
An inherent characteristic of longitudinal data is the dependence among the observations within the same subject. For exhibiting dependencies among the observations within the same subject, this paper considers a semiparametric partially linear regression model for longitudinal data based on martingale difference error's structure. We establish a strong consistency for the least squares estimator of a parametric component and the estimator of a non-parametric function under some mild conditions. A simulation study shows the performance of the proposed estimator in finite samples.
引用
收藏
页码:521 / 534
页数:14
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