Penalized Linear Regression Methods where the Predictors Have Grouping Effect

被引：0

作者：

Jiratchayut, Kanyalin ^{[1
]}

Bumrungsup, Chinnaphong ^{[2
]}

机构：

[1] Burapha Univ, Fac Sci & Arts, Chanthaburi Campus, Tha Mai 22170, Chanthaburi, Thailand

[2] Thammasat Univ, Fac Sci & Technol, Dept Math & Stat, Khlong Luang 12120, Pathum Thani, Thailand

来源：

THAILAND STATISTICIAN | 2019年 / 17卷 / 02期

关键词：

Adaptive elastic net; correlation based penalty; elastic net; SCAD-L-2; variable selection; VARIABLE SELECTION; ELASTIC-NET;

D O I：

暂无

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：

The aim of this paper is to study the performance of four different penalized linear regression methods: elastic net, adaptive elastic net, L1CP, and SCAD-L-2. Simulation studies show that the adaptive elastic net performs best in variable selection and parameter estimation, while the SCAD-L-2 has prediction accuracy better than the other methods. When sample size is large, the L1CP has a prediction performance close to the prediction accuracy of the SCAD-L-2.

引用

页码：212 / 222

页数：11

共 22 条

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