An Extended Single-index Model with Missing Response at Random

被引：7
|
作者：
Wang, Qihua [1 ,2 ]
Zhang, Tao [3 ]
Haerdle, Wolfgang Karl [4 ,5 ]
机构：
[1] Shenzhen Univ, Inst Stat Sci, Shenzhen, Peoples R China
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[3] Guangxi Univ Sci & Technol, Sch Sci, Liuzhou, Guangxi, Peoples R China
[4] Singapore Management Univ, Sch Business, Singapore, Singapore
[5] Humboldt Univ, CASE, Berlin, Germany
来源：
SCANDINAVIAN JOURNAL OF STATISTICS | 2016年 / 43卷 / 04期
基金：
中国国家自然科学基金;
关键词：
asymptotic normality; estimating equations; missing data; single-index models; DIMENSION REDUCTION; EMPIRICAL LIKELIHOOD; REGRESSION; INFERENCE;
D O I：
10.1111/sjos.12233
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 extended single-index model is considered when responses are missing at random. A three-step estimation procedure is developed to define an estimator for the single-index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An algorithm for computing this estimator is proposed. This algorithm only involves one-dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation studies are conducted to investigate the finite sample performances of the proposed estimators.
引用
收藏
页码：1140 / 1152
页数：13
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