Iteratively reweighted least squares: A comparison of several single step algorithms for linear models

DOI10.1007/BF02074884zbMath0760.65140MaRDI QIDQ1198981

Ralf Wolke

Publication date: 16 January 1993

Published in: BIT (Search for Journal in Brave)

zbMATH Keywords

numerical testslinear modelrobust estimatorsiteratively reweighted least squaresHuber estimator\(\mathcal M\)-estimatorssingle step algorithms

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (3)

Robust registration of point sets using iteratively reweighted least squares ⋮ The memory center ⋮ Recursive robust regression computational aspects and comparison

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A new algorithm for the Huber estimator in linear models
• Algorithms for non-linear Huber estimation
• Finite algorithms for robust linear regression
• Robust Regression Computation Using Iteratively Reweighted Least Squares
• Iteratively Reweighted Least Squares: Algorithms, Convergence Analysis, and Numerical Comparisons
• Finite Algorithms for Huber’sM-Estimator
• Hyperbolic Householder Transforms
• Numerical solution of robust regression problems: computational aspects, a comparison
• On the structure of zero finders
• Numerical Methods for Robust Regression: Linear Models
• A Note on Downdating the Cholesky Factorization
• Calculation of linear bestL p -approximations

This page was built for publication: Iteratively reweighted least squares: A comparison of several single step algorithms for linear models