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

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.