Asymptotic Equivalence between Cross-Validations and Akaike Information Criteria in Mixed-Effects Models

[摘要] For model selection in mixed effects models, Vaida and Blan chard (2005) demonstrated that the marginal Akaike information criterion is appropriate as to the questions regarding the population and the conditional Akaike information criterion is appropriate as to the questions regarding the particular clusters in the data. This article shows that the marginal Akaike information criterion is asymptotically equivalent to the leave-one-cluster-out cross-validation and the conditional Akaike information criterion is asymptotically equivalent to the leave-one-observation-out cross-validation.

[学科分类] 土木及结构工程学

[关键词] AIC;degrees of freedom;functional data;model selection