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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 PublicationSCANDINAVIAN JOURNAL OF STATISTICS
ISSN0303-6898
Volume45Issue:2Pages:324-346
AbstractIt 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.
Keywordborrowing missingness information missing data ultrahigh dimensionality variable screening
DOI10.1111/sjos.12290
Language英语
Funding ProjectNational 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 AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000432032100005
PublisherWILEY
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30289
Collection应用数学研究所
Affiliation1.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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