Regularized REML for Estimation in Heteroscedastic Regression Models

被引：0
|
作者：
Xu, Dengke [1 ]
Zhang, Zhongzhan [1 ]
机构：
[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
来源：
NONLINEAR MATHEMATICS FOR UNCERTAINTY AND ITS APPLICATIONS | 2011年 / 100卷
关键词：
Heteroscedastic regression models; Variable selection; REML; Regularization; VARIABLE SELECTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a regularized restricted maximum likelihood(REML) method for simultaneous variable selection in heteroscedastic regression models. Under certain regularity conditions, we establish the consistency and asymptotic normality of the resulting estimator. A simulation study is conducted to illustrate the performance of the proposed method.
引用
收藏
页码：495 / 502
页数：8
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