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A resampling design for computing high-breakdown regression

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Author Info
Rousseeuw, Peter J.

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Abstract

For the computation of high-breakdown (HB) regression one typically uses an algorithm based on randomly selected p-subsets, where p is the number of parameters. This resampling algorithm may itself break down, with a probability that decreases with the number of p-subsets generated. In order to be certain that this algorithm does not break down, the number of p-subsets needs to be O(np). In this paper a resampling design is proposed such that for fixed p the necessary number of p-subsets is merely O(n). This resampling design can also be used for HB nonlinear regression and for HB estimators of multivariate location and scatter.

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File URL: http://www.sciencedirect.com/science/article/B6V1D-45GVX8D-6/2/266c86575db6bb1f869bfd3cba65c6c1
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Publisher Info
Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 18 (1993)
Issue (Month): 2 (September)
Pages: 125-128
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Handle: RePEc:eee:stapro:v:18:y:1993:i:2:p:125-128

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Related research
Keywords: Breakdown point computation time robust regression;

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This page was last updated on 2009-12-30.

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