KMS Of Academy of mathematics and systems sciences, CAS
| How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response? | |
Wang, Qihua1,2 ; Li, Yongjin1
| |
| 2018-06-01 | |
| Source Publication | SCANDINAVIAN JOURNAL OF STATISTICS
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| ISSN | 0303-6898 |
| Volume | 45Issue:2Pages:324-346 |
| Abstract | It is quite a challenge to develop model-free feature screening approaches for missing response problems because the existing standard missing data analysis methods cannot be applied directly to high dimensional case. This paper develops some novel methods by borrowing information of missingness indicators such that any feature screening procedures for ultrahigh-dimensional covariates with full data can be applied to missing response case. The first method is the so-called missing indicator imputation screening, which is developed by proving that the set of the active predictors of interest for the response is a subset of the active predictors for the product of the response and missingness indicator under some mild conditions. As an alternative, another method called Venn diagram-based approach is also developed. The sure screening property is proven for both methods. It is shown that the complete case analysis can also keep the sure screening property of any feature screening approach with sure screening property. |
| Keyword | borrowing missingness information missing data ultrahigh dimensionality variable screening |
| DOI | 10.1111/sjos.12290 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[11171331] ; National Natural Science Foundation of China[11331011] ; National Natural Science Foundation of China[61621003] ; Key Lab of Random Complex Structure and Data Science, CAS ; Natural Science Foundation of SZU |
| WOS Research Area | Mathematics |
| WOS Subject | Statistics & Probability |
| WOS ID | WOS:000432032100005 |
| Publisher | WILEY |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/30289 |
| Collection | 应用数学研究所 |
| Corresponding Author | Wang, Qihua |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Shenzhen Univ, Inst Stat Sci, Shenzhen, Peoples R China |
| Recommended Citation GB/T 7714 | Wang, Qihua,Li, Yongjin. How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?[J]. SCANDINAVIAN JOURNAL OF STATISTICS,2018,45(2):324-346. |
| APA | Wang, Qihua,&Li, Yongjin.(2018).How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?.SCANDINAVIAN JOURNAL OF STATISTICS,45(2),324-346. |
| MLA | Wang, Qihua,et al."How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?".SCANDINAVIAN JOURNAL OF STATISTICS 45.2(2018):324-346. |
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