A robust proposal of estimation for the sufficient dimension reduction problem

DOI10.1007/s11749-020-00745-9zbMath1474.62096OpenAlexW3120689602MaRDI QIDQ2666070

Andrea Bergesio, María Eugenia Szretter Noste, Víctor J. Yohai

Publication date: 22 November 2021

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-020-00745-9

zbMATH Keywords

robustnessprincipal fitted components\( \tau \)-estimatorsmultivariate reduced-rank regression

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

tauPFC

Uses Software

• R
• alr3
• robustbase
• LDR
• ElemStatLearn
• rrcov
• GitHub

Cites Work

• Unnamed Item
• Unnamed Item
• Robust reduced-rank modeling via rank regression
• Fisher lecture: Dimension reduction in regression
• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• A robust predictive approach for canonical correlation analysis
• High breakdown-point and high efficiency robust estimates for regression
• Dimension estimation in sufficient dimension reduction: a unifying approach
• Principal fitted components for dimension reduction in regression
• Robust dimension reduction based on canonical correlation
• A distribution-free M-estimator of multivariate scatter
• Robustness of estimators on stationary observations
• Reduced-rank regression for the multivariate linear model
• Multivariate reduced-rank regression
• Optimal locally robust M-estimates of regression
• Rank estimation in reduced-rank regression
• Marginal integration \(M\)-estimators for additive models
• Using DAGs to identify the sufficient dimension reduction in the principal fitted components model
• Robust nonparametric regression estimation for dependent observations
• On the uniqueness of \(S\)-functionals and \(M\)-functionals under nonelliptical distributions.
• Model-based SIR for dimension reduction
• Robust estimation for the multivariate linear model based on a \(\tau\)-scale
• Contour regression: a general approach to dimension reduction
• Estimating the Structural Dimension of Regressions Via Parametric Inverse Regression
• Outlier resistant estimators for canonical correlation analysis
• On Directional Regression for Dimension Reduction
• High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
• Sliced Inverse Regression for Dimension Reduction
• Multivariate τ-Estimators for Location and Scatter
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Robust estimates for arch processes
• Applied Linear Regression
• Sufficient Reductions in Regressions With Elliptically Contoured Inverse Predictors
• Robust reduced-rank regression
• A General Qualitative Definition of Robustness
• Sufficient Dimension Reduction via Inverse Regression
• Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions
• Comment
• Robust Statistics