How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?

doi:10.1111/sjos.12290

CSpace > 应用数学研究所

	How to Make Model-free Feature Screening Approaches for Full Data Applicable to the Case of Missing Response?
	Wang, Qihua1,2 ; Li, Yongjin 1
	2018-06-01
发表期刊	SCANDINAVIAN JOURNAL OF STATISTICS
ISSN	0303-6898
卷号	45 期号:2 页码:324-346
摘要	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.
关键词	borrowing missingness information missing data ultrahigh dimensionality variable screening
DOI	10.1111/sjos.12290
语种	英语
资助项目	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研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000432032100005
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/30289
专题	应用数学研究所
通讯作者	Wang, Qihua
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Shenzhen Univ, Inst Stat Sci, Shenzhen, Peoples R China
推荐引用方式 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.