Akaike Information Criterion for Selecting Variables in the Nested Error Regression Model

被引：1

作者：

Kubokawa, Tatsuya ^{[1
]}

Srivastava, Muni S. ^{[2
]}

机构：

[1] Univ Tokyo, Fac Econ, Bunkyo Ku, Tokyo 1130033, Japan

[2] Univ Toronto, Dept Stat, Toronto, ON, Canada

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2012年 / 41卷 / 15期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Akaike information criterion; Analysis of variance; Linear mixed model; Nested error regression model; Random effect; Selection of variables; Small area estimation;

D O I：

10.1080/03610926.2011.555043

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：

The Akaike Information Criterion (AIC) is developed for selecting the variables of the nested error regression model where an unobservable random effect is present. Using the idea of decomposing the likelihood into two parts of "within" and "between" analysis of variance, we derive the AIC when the number of groups is large and the ratio of the variances of the random effects and the random errors is an unknown parameter. The proposed AIC is compared, using simulation, with Mallows' C-p, Akaike's AIC, and Sugiura's exact AIC. Based on the rates of selecting the true model, it is shown that the proposed AIC performs better.

引用

页码：2626 / 2642

页数：17

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