Comment on “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

DOI10.1080/01621459.2020.1837141zbMath1452.62521OpenAlexW3117151583MaRDI QIDQ5146021

Po-Ling Loh

Publication date: 22 January 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1837141

zbMATH Keywords

robustnessefficiencylinear regressionhigh dimensiontuning parameterheavy-tailed error

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Cites Work

• Unnamed Item
• A minimax-bias property of the least \(\alpha\)-quantile estimates
• Sub-Gaussian estimators of the mean of a random vector
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries
• Least Median of Squares Regression
• Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals
• Robust Estimation via Robust Gradient Estimation
• Robust Estimates of Location: Symmetry and Asymmetric Contamination
• Robust Estimation of Straight Line Regression Coefficients by Minimizing pth Power Deviations
• Robust Statistics

This page was built for publication: Comment on “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”