Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation

DOI10.1016/S0378-3758(99)00102-0zbMath0976.62075OpenAlexW2081850149WikidataQ127293061 ScholiaQ127293061MaRDI QIDQ1970860

Douglas P. Wiens

Publication date: 7 June 2000

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(99)00102-0

zbMATH Keywords

robustnessD-optimalitypolynomial regressionA-optimalityLegendre polynomialweighted least squaresmultiple regressionleast median of squaresQ-optimalitybounded influence M-estimation

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Optimal statistical designs (62K05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (18)

Minimax weights for generalised M-estimation in biased regression models ⋮ Risk-Adapted Optimal Experimental Design ⋮ A batch ensemble approach to active learning with model selection ⋮ Active Learning with Multiple Localized Regression Models ⋮ Improving importance estimation in pool-based batch active learning for approximate linear regression ⋮ Robust adaptive filtering based on M-estimation-based minimum error entropy criterion ⋮ Prediction of X-ray fluorescence copper grade using regularized stochastic configuration networks ⋮ Pool-based active learning in approximate linear regression ⋮ Jackknifing, weighting, diagnostics and variance estimation in generalized \(M\)-estimation ⋮ Restricted minimax robust designs for misspecified regression models ⋮ Model-robust and model-sensitive designs ⋮ A-minimax and D-minimax robust optimal designs for approximately linear Haar-wavelet models ⋮ On Prior Selection and Covariate Shift of β-Bayesian Prediction Under α-Divergence Risk ⋮ Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search ⋮ Semi-supervised speaker identification under covariate shift ⋮ Dimensionality reduction for density ratio estimation in high-dimensional spaces ⋮ Efficient exploration through active learning for value function approximation in reinforcement learning ⋮ Designs for weighted least squares regression, with estimated weights

Cites Work

• Asymptotics of generalized \(M\)-estimation of regression and scale with fixed carriers, in an approximately linear model
• Optimal robust designs: Linear regression in \(R^ k\).
• Minimax designs for approximately linear regression
• Reweighting approximate GM estimators: Asymptotics and residual-based graphics
• Robust designs for approximately polynomial regression
• Minimax regression designs for approximately linear models with autocorrelated errors
• Moving local regression: The weight function
• Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors
• Optimal Experimental Design for Polynomial Regression
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation