Model selection using information criteria under a new estimation method: least squares ratio

被引：4

作者：

Deniz, Eylem ^{[1
]}

Akbilgic, Oguz ^{[2
]}

Howe, J. Andrew

机构：

[1] Mimar Sinan Fine Arts Univ, Fac Sci & Letters, Dept Stat, Istanbul, Turkey

[2] Istanbul Univ, Fac Business Adm, Dept Quantitat Tech, Istanbul, Turkey

来源：

JOURNAL OF APPLIED STATISTICS | 2011年 / 38卷 / 09期

关键词：

model selection; least squares ratio; subset selection; information criteria; LINEAR-REGRESSION; COMPLEXITY;

D O I：

10.1080/02664763.2010.545111

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：

In this study, we evaluate several forms of both Akaike-type and Information Complexity (ICOMP)-type information criteria, in the context of selecting an optimal subset least squares ratio (LSR) regression model. Our simulation studies are designed to mimic many characteristics present in real data - heavy tails, multicollinearity, redundant variables, and completely unnecessary variables. Our findings are that LSR in conjunction with one of the ICOMP criteria is very good at selecting the true model. Finally, we apply these methods to the familiar body fat data set.

引用

页码：2043 / 2050

页数：8

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