LASSO-type variable selection methods for high-dimensional data

被引：1

作者：

Fu, Guanghui ^{[1
]}

Wang, Pan ^{[1
]}

机构：

[1] Kunming Univ Sci & Technol, Coll Sci, Kunming 650500, Peoples R China

来源：

ADVANCES IN COMPUTATIONAL MODELING AND SIMULATION, PTS 1 AND 2 | 2014年 / 444-445卷

关键词：

LASSO; Variable selection; High-dimensional data; Oracle property; Group effect; ORACLE PROPERTIES; ELASTIC-NET; REGRESSION; SHRINKAGE;

D O I：

10.4028/www.scientific.net/AMM.0.604

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

LASSO is a very useful variable selection method for high-dimensional data, But it does not possess oracle property[Fan and Li, 2001] and group effect[Zou and Hastie, 2005]. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and(or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

引用

页码：604 / 609

页数：6

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