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An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1977 · Vol. 39(1) · pp. 44–47
M. Stone✉(UCL Australia)
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.
Engineering Applied ResearchAkaike information criterionEquivalence (formal languages)Bayesian information criterionMathematicsCross-validationStatisticsEconometricsApplied mathematicsPure mathematics
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References
Cross-Validatory Choice and Assessment of Statistical Predictions
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1974 · 10,283 citations
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