COMPUTATIONAL STATISTICS & DATA ANALYSIS vol:54 issue:12 pages:3121-3130
Robust estimators of the prediction error of a linear model are proposed.
The estimators are based on the resampling techniques cross-validation and
bootstrap. The robustness of the prediction error estimators is obtained by
robustly estimating the regression parameters of the linear model and by
trimming the largest prediction errors. To avoid the recalculation of time-consuming robust regression estimates, fast approximations for the robust
estimates of the resampled data are used. This leads to time efficient and
robust estimators of prediction error.