Algorithms for non-linear Huber estimation
type: Item
instance of: scholarly article
title: Algorithms for non-linear Huber estimation
zbMATH Open document ID: 0677.65148
DOI: 10.1007/BF01932706
author: Kaj Madsen
author: Hakan Ekblom
published in: BIT
publication date: 1989-01-01T00:00:00Z
review text: The estimation of \textit{P. J. Huber} [Robust statistics (1981; Zbl 0536.62025)] is a modification of least squares estimation designed to reduce the influence of data contamination. Numerical determination of the estimate requires minimization of a sum of \(\rho (f_ j(x))\) where \(\rho\) is the Huber function, and \(f_ j\) are (in general non-linear) regression functions of the parameter vector x. The authors present and investigate algorithms for this task.
Mathematics Subject Classification ID: 65C99
Mathematics Subject Classification ID: 65D10
Mathematics Subject Classification ID: 62F35
Mathematics Subject Classification ID: 62J02
zbMATH DE Number: 4110063
zbMATH Keywords: non-linear Huber estimation
zbMATH Keywords: non-linear robust parameter estimation
zbMATH Keywords: trust region method
zbMATH Keywords: least squares estimation
zbMATH Keywords: data contamination
zbMATH Keywords: regression
zbMATH Keywords: algorithms
reviewed by: Rudolf Wegmann
describes a project that uses: Algorithm 500
MaRDI profile type: Publication
cites work: Numerical methods for the nonlinear robust regression problem
cites work: A new algorithm for the Huber estimator in linear models
cites work: Q3723487
cites work: A method for the solution of certain non-linear problems in least squares
cites work: An Algorithm for Least-Squares Estimation of Nonlinear Parameters
cites work: Ein Verfahren zur Minimierung einer Quadratsumme nichtlinearer Funktionen
cites work: Q4772099
cites work: Numerical Methods for Robust Regression: Linear Models
description: scientific article; zbMATH DE number 4110063