Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Yong Liu


Two theorems and a lemma are presented about the use of jackknife es(cid:173) timator and the cross-validation method for model selection. Theorem 1 gives the asymptotic form for the jackknife estimator. Combined with the model selection criterion, this asymptotic form can be used to obtain the fit of a model. The model selection criterion we used is the negative of the average predictive likehood, the choice of which is based on the idea of the cross-validation method. Lemma 1 provides a formula for further explo(cid:173) ration of the asymptotics of the model selection criterion. Theorem 2 gives an asymptotic form of the model selection criterion for the regression case, when the parameters optimization criterion has a penalty term. Theorem 2 also proves the asymptotic equivalence of Moody's model selection cri(cid:173) terion (Moody, 1992) and the cross-validation method, when the distance measure between response y and regression function takes the form of a squared difference.