A biased-robust regression technique for the combined outlier-multicollinearity problem

DOI10.1080/00949659608811777zbMath0884.62074OpenAlexW1992199580MaRDI QIDQ4346976

Douglas C. Montgomery, James R. Simpson

Publication date: 5 April 1998

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659608811777

zbMATH Keywords

efficiencyMonte Carlo simulationridge regressionbreakdown pointbounded influence

Mathematics Subject Classification ID

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

Related Items (5)

Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators ⋮ Fuzzy-weighted estimation in ridge regression analysis ⋮ Sparse linear regression models of high dimensional covariates with non-Gaussian outliers and Berkson error-in-variable under heteroscedasticity ⋮ Rank-based ridge estimation in multiple linear regression ⋮ Graphical evaluation of the ridge-type robust regression estimators in mixture experiments

Uses Software

• ROBETH

Cites Work

• Unnamed Item
• Robust m-estimators of multivariate location and scatter
• A Bounded Influence, High Breakdown, Efficient Regression Estimator
• An analysis of constrained robust regression estimators
• Least Median of Squares Regression
• Augmented Robust Estimators
• Efficient Bounded-Influence Regression Estimation
• On One-Step GM Estimates and Stability of Inferences in Linear Regression
• Ridge regression iterative estimation of the biasing parameter
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Robust estimation: A condensed partial survey
• Robust Statistics

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