Nonparametric feature screening

被引：25

作者：

Lin, Lu ^{[1
,2
]}

Sun, Jing ^{[2
,5
]}

Zhu, Lixing ^{[3
,4
]}

机构：

[1] Shandong Univ, Qilu Secur Inst Financial Studies, Jinan, Peoples R China

[2] Shandong Univ, Sch Math, Jinan, Peoples R China

[3] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China

[4] Yunnan Univ Finance & Econ, Kunming, Peoples R China

[5] Ludong Univ, Sch Math & Stat Sci, Yantai, Shandong, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2013年 / 67卷

关键词：

Ultrahigh-dimensional data; Function-correlation; Feature screening; Marginal utility; Nonparametric model;

D O I：

10.1016/j.csda.2013.05.016

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The measure of correlation between response and predictors plays a critical role in feature ranking and screening for nonparametric regression models. In this paper, a nonparametric function-correlative feature screening is introduced. The newly proposed method does not need any assumption on structural relationships between response and predictors, and among predictors. By using local information flows of model variables, the function-correlation between response and predictors is captured successfully. Selection consistency is achieved as well. Simulation studies are carried out to examine the performance of the new method. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：162 / 174

页数：13

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