Mean response estimation with missing response in the presence of high-dimensional covariates
文献类型：期刊论文
| 作者 | Li, Yongjin1; Wang, Qihua1,2; Zhu, Liping3; Ding, Xiaobo1
|
| 刊名 | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
 |
| 出版日期 | 2017 |
| 卷号 | 46期号:2页码:628-643 |
| 关键词 | Central mean subspace Imputation Kernel regression Missing response Weighted-bandwidth |
| ISSN号 | 0361-0926 |
| DOI | 10.1080/03610926.2014.1002935 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000386396500010 |
| 出版者 | TAYLOR & FRANCIS INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23849]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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