A class of robust and fully efficient regression estimators
type: Item
instance of: scholarly article
title: A class of robust and fully efficient regression estimators
zbMATH Open document ID: 1012.62073
author: Daniel Gervini
author: Victor J. Yohai
published in: The Annals of Statistics
publication date: 2002-11-14T00:00:00Z
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F35
Mathematics Subject Classification ID: 62H12
zbMATH DE Number: 1829094
Wikidata QID: Q58194162
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1214/aos/1021379866
OpenAlex ID: W1997815969
cites work: A one-step robust estimator for regression based on the weighted likelihood reweighting scheme
cites work: The asymptotics of S-estimators in the linear regression model
cites work: Q3673862
cites work: Q4119973
cites work: Q3723487
cites work: Reweighted LS estimators converge at the same rate as the initial estimator
cites work: On the optimality of S-estimators
cites work: Min-max bias robust regression
cites work: Q4001821
cites work: Q3343267
cites work: Q4692741
cites work: Q3683344
cites work: Functional stability of one-step GM-estimators in approximately linear regression
cites work: High breakdown-point and high efficiency robust estimates for regression
cites work: High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
cites work: A minimax-bias property of the least \(\alpha\)-quantile estimates
DOI: 10.1214/AOS/1021379866
description: scientific article; zbMATH DE number 1829094