Conditional information criteria for selecting variables in linear mixed models

被引:12
作者
Srivastava, Muni S. [2 ]
Kubokawa, Tatsuya [1 ]
机构
[1] Univ Tokyo, Fac Econ, Bunkyo Ku, Tokyo 1130033, Japan
[2] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada
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;
D O I
10.1016/j.jmva.2010.05.007
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 paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained from many clusters. As the variable selection procedure, here we use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC and conditional AIC. In this paper, we derive three versions of conditional AIC depending upon different estimators of the regression coefficients and the random effects. Through the simulation studies, it is shown that the proposed conditional AIC's are superior to the marginal and conditional AIC's proposed in the literature in the sense of selecting the true model. Finally, the results are extended to the case when the random effects in all the clusters are of the same dimension but have a common unknown covariance matrix. (c) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:1970 / 1980
页数:11
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