Maximum bias curves for robust regression with non-elliptical regressors
From MaRDI portal
Publication:1848860
DOI10.1214/aos/996986507zbMath1029.62028OpenAlexW1971762149MaRDI QIDQ1848860
Ruben H. Zamar, José R. Berrendero
Publication date: 14 November 2002
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/996986507
Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)
Related Items
The maximum asymptotic bias of S-estimates for regression over the neighborhoods defined by certain special capacities ⋮ Robust tests for linear regression models based on \(\tau\)-estimates ⋮ APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood ⋮ Robust and efficient estimation of the residual scale in linear regression ⋮ High-breakdown robust multivariate methods ⋮ Globally robust inference for the location and simple linear regression models ⋮ Bias robustness of three median-based regression estimates. ⋮ On the maximum bias functions of \(MM\)-estimates and constrained \(M\)-estimates of regression ⋮ Bias bound for the minimax estimator ⋮ Empirical analysis of the maximum asymptotic bias of location estimators for fuzzy number-valued data ⋮ A parametric framework for the comparison of methods of very robust regression
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bias-robust estimates of regression based on projections
- Bias robust estimation of scale
- Min-max bias robust regression
- A local breakdown property of robust tests in linear regression
- On the optimality of S-estimators
- Bias robust estimation in orthogonal regression
- Stability under contamination of robust regression estimators based on differences of residuals.
- Minimax bias-robust estimation of the dispersion matrix of a multivariate distribution
- A minimax-bias property of the least \(\alpha\)-quantile estimates
- Asymptotics of generalized \(S\)-estimators
- Optimal locally robust M-estimates of regression
- Globally robust inference for the location and simple linear regression models
- Functional stability of one-step GM-estimators in approximately linear regression
- The ``automatic robustness of minimum distance functionals
- Positive-breakdown regression by minimizing nested scale estimators
- Lower bounds for contamination bias: Globally minimax versus locally linear estimation
- Least Median of Squares Regression
- Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
- High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
- On One-Step GM Estimates and Stability of Inferences in Linear Regression
- Rank-Based Estimates in the Linear Model with High Breakdown Point
- Regression Depth
- Robust Estimation of a Location Parameter
- Robust Statistics
