On the optimality of prediction-based selection criteria and the convergence rates of estimators

Several estimators of squared prediction error have been suggested for use in model and bandwidth selection problems. Among these are cross-validation, generalized cross-validation and a number of related techniques based on the residual sum of squares. For many situations with squared error loss, e.g. nonparametric smoothing, these estimators have been shown to be asymptotically optimal in the sense that in large samples the estimator minimizing the selection criterion also minimizes squared error loss. However, cross-validation is known not to be asymptotically optimal for some 'easy' location problems. We consider selection criteria based on estimators of squared prediction risk for choosing between location estimators. We show that criteria based on adjusted residual sum of squares are not asymptotically optimal for choosing between asymptotically normal location estimators that converge at rate n(1/2) but are when the rate of convergence is slower. We also show that leave-one-out cross-validation is not asymptotically optimal for choosing between root n-differentiable statistics but leave-d-out cross-validation is optimal when d --> infinity at the appropriate rate.