Model selection of M-estimation models using least squares approximation

被引：0
|
作者：
Mao, Guangyu [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China
来源：
STATISTICS & PROBABILITY LETTERS | 2015年 / 99卷
关键词：
Information criterion; Least squares approximation; M-estimation; Model selection;
D O I：
10.1016/j.spl.2015.01.027
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 proposes a new criterion for the M-estimation models based on a least squares approximation, which is proved to be selection consistent. Compared with the existing criteria, this new one has two attractive features. One is that model selection based on it has much lower computational cost. The other is that it may bring considerable improvement in some cases since it is essentially based on the efficient GMM estimation. (C) 2015 Elsevier B.V. All rights reserved.
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页码：238 / 243
页数：6
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