Robust Sparse Regression with High-Breakdown Value

DOI10.1080/03610926.2012.750357zbMath1325.62063OpenAlexW1990666290MaRDI QIDQ5259110

Weiyan Mu, Shifeng Xiong

Publication date: 24 June 2015

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2012.750357

zbMATH Keywords

adaptive estimationvariable selectionbreakdown valuenon negative garrote

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

On the robustness of Mallows’ C_p criterion

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Robust and sparse bridge regression
• Small sample corrections for LTS and MCD
• A class of robust and fully efficient regression estimators
• Outlier Detection Using Nonconvex Penalized Regression
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Inconsistency of Resampling Algorithms for High-Breakdown Regression Estimators and a New Algorithm
• Asymptotic Analysis of Robust LASSOs in the Presence of Noise With Large Variance
• Robust Statistics

This page was built for publication: Robust Sparse Regression with High-Breakdown Value