Mean response estimation with missing response in the presence of high-dimensional covariates

doi:10.1080/03610926.2014.1002935

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	Mean response estimation with missing response in the presence of high-dimensional covariates
	Li, Yongjin 1; Wang, Qihua 1,2; Zhu, Liping 3; Ding, Xiaobo1
	2017
发表期刊	COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
ISSN	0361-0926
卷号	46 期号:2 页码:628-643
摘要	This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations.
关键词	Central mean subspace Imputation Kernel regression Missing response Weighted-bandwidth
DOI	10.1080/03610926.2014.1002935
语种	英语
资助项目	National Science Fund for Distinguished Young Scholars in China[10725106] ; National Natural Science Foundation of China[11171331] ; National Natural Science Foundation of China[11331011] ; National Natural Science Foundation of China[11371236] ; National Natural Science Foundation of China[11422107] ; National Natural Science Foundation of China[11201457] ; Natural Science Foundation of SZU ; Henry Fok Education Foundation Fund of Young College Teachers[141002] ; Programs for New Century Excellent Talents[NCET-12-0901] ; Innovative Research Team in University of China[IRT13077] ; Ministry of Education of China ; Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000386396500010
出版者	TAYLOR & FRANCIS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23850
专题	应用数学研究所
通讯作者	Ding, Xiaobo
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Shenzhen Univ, Inst Stat Sci, Shenzhen, Guangdong, Peoples R China 3.Renmin Univ China, Inst Stat & Big Data, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Yongjin,Wang, Qihua,Zhu, Liping,et al. Mean response estimation with missing response in the presence of high-dimensional covariates[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,2017,46(2):628-643.
APA	Li, Yongjin,Wang, Qihua,Zhu, Liping,&Ding, Xiaobo.(2017).Mean response estimation with missing response in the presence of high-dimensional covariates.COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,46(2),628-643.
MLA	Li, Yongjin,et al."Mean response estimation with missing response in the presence of high-dimensional covariates".COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 46.2(2017):628-643.