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Conditional Information Criteria for Selecting Variables in Linear Mixed Models

Author

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  • Muni S. Srivastava

    (Department of Statistics, University of Toronto)

  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

Abstract

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, we here 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.

Suggested Citation

  • Muni S. Srivastava & Tatsuya Kubokawa, 2008. "Conditional Information Criteria for Selecting Variables in Linear Mixed Models," CIRJE F-Series CIRJE-F-575, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2008cf575
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