On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models
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 no longer an asymptotically unbiased estimator of the Akaike information, and in fact 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 leads 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 an R package 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.
Statistical Methodology | Statistical Theory
Greven, Sonja and Kneib, Thomas, "On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models" (November 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 202.
Supplementary material, including proofs and additional simulation and application results
cAIC.zip (36 kB)
R package implementing the corrected cAIC