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A Monte Carlo comparison of several high breakdown and efficient estimators. (English) Zbl 1061.62524

Summary: High breakdown point, bounded influence and high efficiency at the Gaussian model are desired properties of robust regression estimators. Several estimators have been proposed to achieve at least some of these properties and their asymptotic behavior has been derived in the literature. In this article, the comparison of the finite sample performance of these estimators is carried out by a Monte Carlo study for a number of outlier-generating models. Some striking finite sample behavior is found in most of the high breakdown point estimators. Only SLS-estimator provides consistent performance in both zero-slope and non-zero-slope cases and is the best for the latter case.

MSC:

62F35 Robustness and adaptive procedures (parametric inference)
62C05 General considerations in statistical decision theory
62J05 Linear regression; mixed models
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