Constrained regression model selection

被引：0
|
作者：
Li, Lexin [1 ]
Tsai, Chih-Ling [2 ]
机构：
[1] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
[2] Univ Calif Davis, Grad Sch Management, Davis, CA 95616 USA
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2008年 / 138卷 / 12期
关键词：
model selection; parameter constraint; single-index model;
D O I：
10.1016/j.jspi.2008.02.006
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：
We propose two improved versions of the Akaike information criterion, AIC(C) and AIC(C)*, for the constrained linear and sin le-index models, respectively. These enhanced versions have corresponding unconstrained selection criteria as their special cases. Our Monte Carlo simulations demonstrate that AIC(C) and AIC(C)* are superior to the Akaike information criterion. Additionally, we illustrate the use of AIC(C)* in an empirical example and generalize AIC(C)* to the constrained partially linear model. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码：3939 / 3949
页数：11
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