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A resampling design for computing high-breakdown regression

Peter Rousseeuw ()

Statistics & Probability Letters, 1993, vol. 18, issue 2, 125-128

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.

Keywords: Breakdown; point; computation; time; robust; regression (search for similar items in EconPapers)
Date: 1993
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