Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)∗

The paper presents results from simulations based on real data, comparing several competing mean squared error of prediction (MSEP) estimators on principal components regression (PCR) and partial least squares regression (PLSR): leave-one-out crossvalidation, K-fold and adjusted K-fold cross-validation, the ordinary bootstrap estimate, the bootstrap smoothed cross-validation (BCV) estimate and the 0.632 bootstrap estimate. The overall performance of the estimators is compared in terms of their bias, variance and squared error. The results indicate that the 0.632 estimate and leave-one-out crossvalidation are preferable when one can afford the computation. Otherwise adjusted 5or 10-fold cross-validation are good candidates because of their computational efficiency.