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Mean response estimation with missing response in the presence of high-dimensional covariates
Li, Yongjin1; Wang, Qihua1,2; Zhu, Liping3; Ding, Xiaobo1
2017
Source PublicationCOMMUNICATIONS IN STATISTICS-THEORY AND METHODS
ISSN0361-0926
Volume46Issue:2Pages:628-643
AbstractThis 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.
KeywordCentral mean subspace Imputation Kernel regression Missing response Weighted-bandwidth
DOI10.1080/03610926.2014.1002935
Language英语
Funding ProjectNational 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 Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000386396500010
PublisherTAYLOR & FRANCIS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/23850
Collection应用数学研究所
Corresponding AuthorDing, Xiaobo
Affiliation1.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
Recommended Citation
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.
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