Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression

We study the problem of estimating the distribution of the out-of-sample prediction error associated with ridge regression. In contrast, the traditional object of study is the uncentered second moment of this distribution (the mean squared prediction error), which can be estimated using cross-validation methods. We show that both generalized and leave-one-out cross-validation (GCV and LOOCV) for ridge regression can be suitably extended to estimate the full error distribution. This is still possible in a high-dimensional setting where the ridge regularization parameter is zero. In an asymptotic framework in which the feature dimension and sample size grow proportionally, we prove that almost surely, with respect to the training data, our estimators (extensions of GCV and LOOCV) converge weakly to the true out-of-sample error distribution. This result requires mild assumptions on the response and feature distributions. We also establish a more general result that allows us to estimate certain functionals of the error distribution, both linear and nonlinear. This yields various applications, including consistent estimation of the quantiles of the out-of-sample error distribution, which gives rise to prediction intervals with asymptotically exact coverage conditional on the training data.