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On the behaviour of marginal and conditional AIC in linear mixed models. (English) Zbl 1204.62114
Summary: In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, AIC, have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that can lead to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in the R package [R Development Core Team (2010)] is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

62J05 Linear regression; mixed models
62P10 Applications of statistics to biology and medical sciences; meta analysis
62H12 Estimation in multivariate analysis
65C60 Computational problems in statistics (MSC2010)
62J10 Analysis of variance and covariance (ANOVA)
R; SemiPar
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