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Linear Model Selection by Cross-Validation
Journal of the American Statistical Association
Vol. 88, No. 422 (Jun., 1993), pp. 486-494
Stable URL: http://www.jstor.org/stable/2290328
Page Count: 9
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We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n → ∞. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-nν-out cross-validation with nν, the number of observations reserved for validation, satisfying nν/n → 1 as n → ∞. This is a somewhat shocking discovery, because nν/n → 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-nν-out cross-validation method are provided, and results from a simulation study are presented.
Journal of the American Statistical Association © 1993 American Statistical Association