Estimation in Partially Linear Single-Index Models with Missing Covariates

被引：5
|
作者：
Liu, X. H. [1 ,2 ]
Wang, Z. Z. [1 ]
Hu, X. M. [3 ,4 ]
机构：
[1] Cent S Univ, Sch Math Sci & Comp Technol, Hunuan 410075, Peoples R China
[2] Jiangxi Univ Finance & Econ, Sch Stat, Nanchang, Jiangxi, Peoples R China
[3] Chongqing Technol & Business Univ, Math & Stat Coll, Chongqing, Peoples R China
[4] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2012年 / 41卷 / 18期
基金：
美国国家科学基金会;
关键词：
Local linear regression; Missing at random; Partially linear single-index model; Weighted estimating equations; EMPIRICAL LIKELIHOOD; REGRESSION;
D O I：
10.1080/03610926.2011.560736
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 article, we consider a partially linear single-index model Y = g(Z(theta 0)(tau)) + X-beta 0(tau) + epsilon when the covariate X may be missing at random. We propose weighted estimators for the unknown parametric and nonparametric part by applying weighted estimating equations. We establish normality of the estimators of the parameters and asymptotic expansion for the estimator of the nonparametric part when the selection probabilities are unknown. Simulation studies are also conducted to illustrate the finite sample properties of these estimators.
引用
收藏
页码：3428 / 3447
页数：20
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