A class of robust and fully efficient regression estimators

This paper introduces a new class of robust estimators for the linear regression model. They are weighted least squares estimators, with weights adaptively computed using the empirical distribution of the residuals of an initial robust estimator. It is shown that under certain general conditions the...

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Autores principales: Gervini, D., Yohai, V.J.
Formato: JOUR
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Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00905364_v30_n2_p583_Gervini
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spelling todo:paper_00905364_v30_n2_p583_Gervini2023-10-03T14:54:40Z A class of robust and fully efficient regression estimators Gervini, D. Yohai, V.J. Adaptive estimation Efficient estimation Maximum breakdown point Weighted least squares This paper introduces a new class of robust estimators for the linear regression model. They are weighted least squares estimators, with weights adaptively computed using the empirical distribution of the residuals of an initial robust estimator. It is shown that under certain general conditions the asymptotic breakdown points of the proposed estimators are not less than that of the initial estimator, and the finite sample breakdown point can be at most 1/n less. For the special case of the least median of squares as initial estimator, hard rejection weights and normal errors and carriers, the maximum bias function of the proposed estimators for point-mass contaminations is numerically computed, with the result that there is almost no worsening of bias. Moreover - and this is the original contribution of this paper - if the errors are normally distributed and under fairly general conditions on the design the proposed estimators have full asymptotic efficiency. A Monte Carlo study shows that they have better behavior than the initial estimators for finite sample sizes. Fil:Gervini, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00905364_v30_n2_p583_Gervini
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Adaptive estimation
Efficient estimation
Maximum breakdown point
Weighted least squares
spellingShingle Adaptive estimation
Efficient estimation
Maximum breakdown point
Weighted least squares
Gervini, D.
Yohai, V.J.
A class of robust and fully efficient regression estimators
topic_facet Adaptive estimation
Efficient estimation
Maximum breakdown point
Weighted least squares
description This paper introduces a new class of robust estimators for the linear regression model. They are weighted least squares estimators, with weights adaptively computed using the empirical distribution of the residuals of an initial robust estimator. It is shown that under certain general conditions the asymptotic breakdown points of the proposed estimators are not less than that of the initial estimator, and the finite sample breakdown point can be at most 1/n less. For the special case of the least median of squares as initial estimator, hard rejection weights and normal errors and carriers, the maximum bias function of the proposed estimators for point-mass contaminations is numerically computed, with the result that there is almost no worsening of bias. Moreover - and this is the original contribution of this paper - if the errors are normally distributed and under fairly general conditions on the design the proposed estimators have full asymptotic efficiency. A Monte Carlo study shows that they have better behavior than the initial estimators for finite sample sizes.
format JOUR
author Gervini, D.
Yohai, V.J.
author_facet Gervini, D.
Yohai, V.J.
author_sort Gervini, D.
title A class of robust and fully efficient regression estimators
title_short A class of robust and fully efficient regression estimators
title_full A class of robust and fully efficient regression estimators
title_fullStr A class of robust and fully efficient regression estimators
title_full_unstemmed A class of robust and fully efficient regression estimators
title_sort class of robust and fully efficient regression estimators
url http://hdl.handle.net/20.500.12110/paper_00905364_v30_n2_p583_Gervini
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