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

19771,294 citationsJournal Article

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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.

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Engineering Applied Research

Publication Details

Published in: Journal of the Royal Statistical Society Series B (Statistical Methodology)

Volume 39, Issue 1, pp. 44-47

DOI: 10.1111/j.2517-6161.1977.tb01603.x

Field-Weighted Citation Impact: 37.80

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

Authors

Abstract

Topics & Keywords

Publication Details