Numerical Methods for Robust Regression: Linear Models

DOI10.1137/0907006zbMath0615.65148OpenAlexW1974062364MaRDI QIDQ4723258

David M. Rocke, David F. Shanno

Publication date: 1986

Published in: SIAM Journal on Scientific and Statistical Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/0907006

zbMATH Keywords

Newton's methodleast squares regressioniteratively reweighted least squaresHuber-Dutter algorithmrobust regression problem

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35) Probabilistic methods, stochastic differential equations (65C99)

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Computational aspects of adaptive combination of least squares and least absolute deviations estimators ⋮ A new algorithm for the Huber estimator in linear models ⋮ Finite algorithms for robust linear regression ⋮ Duality in robust linear regression using Huber's \(M\)-estimator ⋮ Algorithms for non-linear Huber estimation ⋮ Recursive robust regression computational aspects and comparison ⋮ Iteratively reweighted least squares: A comparison of several single step algorithms for linear models ⋮ Generation of test problems for L_p- and huber regression ⋮ Efficient sorting during repetitive statistical computations: Algorithms and an application

Uses Software

• GQTPAR