Model selection with data-oriented penalty

被引:23
作者
Bai, ZD
Rao, CR
Wu, Y
机构
[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA
[2] Natl Sun Yat Sen Univ, Dept Math Appl, Kaohsiung, Taiwan
[3] York Univ, Dept Math & Stat, N York, ON M3J 1P3, Canada
关键词
AIC; GIC; linear regression; model selection; variables selection;
D O I
10.1016/S0378-3758(98)00168-2
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 consider the problem of model (or variable) selection in the classical regression model using the GIC (general information criterion), In this method the maximum likelihood is used with a penalty function denoted by C-n, depending on the sample size n and chosen to ensure consistency in the selection of the true model. There are various choices of C-n suggested in the literature on model selection. In this paper we show that a particular choice of C-n based on observed data, which makes it random, preserves the consistency property and provides improved performance over a fixed choice of C-n. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:103 / 117
页数:15
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