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Fast and robust bootstrap

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Abstract

In this paper we review recent developments on a bootstrap method for robust estimators which is computationally faster and more resistant to outliers than the classical bootstrap. This fast and robust bootstrap method is, under reasonable regularity conditions, asymptotically consistent. We describe the method in general and then consider its application to perform inference based on robust estimators for the linear regression and multivariate location-scatter models. In particular, we study confidence and prediction intervals and tests of hypotheses for linear regression models, inference for location-scatter parameters and principal components, and classification error estimation for discriminant analysis.

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Authors and Affiliations

  1. Department of Statistics, UBC, Vancouver, Canada

    Matías Salibián-Barrera

  2. Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281, S9, 9000, Gent, Belgium

    Stefan Van Aelst & Gert Willems

Authors
  1. Matías Salibián-Barrera
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  2. Stefan Van Aelst
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  3. Gert Willems
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Correspondence to Matías Salibián-Barrera.

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Salibián-Barrera, M., Van Aelst, S. & Willems, G. Fast and robust bootstrap. Stat. Meth. & Appl. 17, 41–71 (2008). https://doi.org/10.1007/s10260-007-0048-6

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