Model-free feature screening for ultrahigh-dimensional data conditional on some variables

被引：10
|
作者：
Liu, Yi [1 ,2 ]
Wang, Qihua [1 ,3 ]
机构：
[1] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[2] China Univ Petr, Coll Sci, Qingdao 266580, Peoples R China
[3] Shenzhen Univ, Inst Stat Sci, Shenzhen 518006, Peoples R China
来源：
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2018年 / 70卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Conditional distance correlation; Feature selection; Sure screening property; High-dimensional data; VARYING COEFFICIENT MODELS; DISTANCE CORRELATION; FEATURE-SELECTION;
D O I：
10.1007/s10463-016-0597-2
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, the conditional distance correlation (CDC) is used as a measure of correlation to develop a conditional feature screening procedure given some significant variables for ultrahigh-dimensional data. The proposed procedure is model free and is called conditional distance correlation-sure independence screening (CDC-SIS for short). That is, we do not specify any model structure between the response and the predictors, which is appealing in some practical problems of ultrahigh-dimensional data analysis. The sure screening property of the CDC-SIS is proved and a simulation study was conducted to evaluate the finite sample performances. Real data analysis is used to illustrate the proposed method. The results indicate that CDC-SIS performs well.
引用
收藏
页码：283 / 301
页数：19
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