An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion

Abstract

Summary A logarithmic assessment of the performance of a predicting density is found to lead to asymptotic equivalence of choice of model by cross-validation and Akaike's criterion, when maximum likelihood estimation is used within each model.