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2002•Birkhäuser Basel eBooksRequires access

Robust Bootstrap for S-estimators of Multivariate Regression

Stefan Van Aelst, Gert Willems

Open publisher page 3 citations

Abstract

Classical bootstrap applied to robust regression estimators can be extremely time consuming and the breakdown point of the procedure is lower than that of the estimator itself. In this paper we develop a robust bootstrap for S-estimators of multivariate regression. Through a simulation study it is shown that confidence intervals for the regression coefficients based on the robust bootstrap have good performance compared to other methods. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

About this research paper

What this paper is about

Classical bootstrap applied to robust regression estimators can be extremely time consuming and the breakdown point of the procedure is lower than that of the estimator itself. In this paper we develop a robust bootstrap for S-estimators of multivariate regression. Through a simulation study it is shown that confidence intervals for the regression coefficients based on the robust bootstrap have good performance compared to other methods. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Classical bootstrap applied to robust regression estimators can be extremely time consuming and the breakdown point of the procedure is lower than that of the estimator itself. In this paper we develop a robust bootstrap for S-estimators of multivariate regression. Through a simulation study it is shown that confidence intervals for the regression coefficients based on the robust bootstrap have good performance compared to other methods. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Key concepts: Estimator, Multivariate statistics, Robust regression, Statistics, Confidence interval, Regression, Regression analysis, Mathematics

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