Fast robust estimation of prediction error based on resampling

Authors:
Jafar A. Khan;Stefan Van Aelst;Ruben H. Zamar
Affiliations:
Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh;Department of Applied Mathematics and Computer Science, Ghent University, Belgium;Department of Statistics, University of British Columbia, Canada
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Abstract

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.