Minimax weights for generalised M-estimation in biased regression models

DOI10.2307/3316144zbMath1022.62031OpenAlexW2013915764MaRDI QIDQ4801843

Douglas P. Wiens, Sanjoy Kumar Sinha

Publication date: 14 October 2003

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/97849a697220b6f4833f71b603b54750c3f604c4

zbMATH Keywords

robustnessadaptive weightsbounded influenceSchweppe weightsMallows weights

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (3)

Extrapolation designs with constraints ⋮ Robust sequential designs for nonlinear regression ⋮ Weighted empirical likelihood estimates and their robustness properties

Cites Work

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• Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
• Least Median of Squares Regression
• Minimax Aspects of Bounded-Influence Regression
• Efficient Bounded-Influence Regression Estimation
• Robust Estimation of a Location Parameter
• Robust Estimates of Location: Symmetry and Asymmetric Contamination

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