A New Formula for Faster Computation of the K-Fold Cross-Validation and Good Regularisation Parameter Values in Ridge Regression

Abstract

In the present paper, we prove a new theorem, resulting in an update formula for linear regression model residuals calculating the exact k-fold cross-validation residuals for any choice of cross-validation strategy without model refitting. The required matrix inversions are limited by the cross-validation segment sizes and can be executed with high efficiency in parallel. The well-known formula for leave-one-out cross-validation follows as a special case of the theorem. In situations where the cross-validation segments consist of small groups of repeated measurements, we suggest a heuristic strategy for fast serial approximations of the cross-validated residuals and associated Predicted Residual Sum of Squares ( $PRESS$ ) statistic. We also suggest strategies for efficient estimation of the minimum $PRESS$ value and full $PRESS$ function over a selected interval of regularisation values. The computational effectiveness of the parameter selection for Ridge- and Tikhonov regression modelling resulting from our theoretical findings and heuristic arguments is demonstrated in several applications with real and highly multivariate datasets.

Keywords

• Cross-validation
• GCV
• PRESS statistic
• ridge regression
• SVD
• Tikhonov regularisation