Efficient Penalized Estimation for Linear Regression Model

被引：0

作者：

Mao, Guangyu ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2015年 / 44卷 / 07期

关键词：

GMM; LASSO; Oracle Property; Penalized Least Squares; SCAD; Shrinkage; VARIABLE SELECTION; HETEROSKEDASTICITY; LIKELIHOOD; REGULARIZATION; LASSO;

D O I：

10.1080/03610926.2012.763094

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：

This paper develops new penalized estimation for linear regression model. We prove that the new method, which is referred to as efficient penalized estimation, is selection consistent, and more asymptotically efficient than the original one. Besides, we construct a new selector called efficient BIC Selector to tune the regularization parameter in the new estimation, which is shown to be consistent. Our simulation results suggest that the new method may bring significant improvement relative to the original penalized estimation. In addition, we employ a real data set to illustrate the application of the efficient penalized estimation.

引用

页码：1436 / 1449

页数：14

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