Minimum distance Lasso for robust high-dimensional regression

DOI10.1214/16-EJS1136zbMath1349.62322OpenAlexW2405968727MaRDI QIDQ286223

Eunho Yang, Nicolai Meinshausen, Aurélie C. Lozano

Publication date: 20 May 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1463664092

zbMATH Keywords

robust estimationLassohigh-dimensional variable selectionsparse learning

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Linear regression; mixed models (62J05)

Related Items

The main contributions of robust statistics to statistical science and a new challenge, A User-Friendly Computational Framework for Robust Structured Regression with the L₂ Criterion, Robust regression against heavy heterogeneous contamination, A semi-parametric approach to feature selection in high-dimensional linear regression models, High-dimensional composite quantile regression: optimal statistical guarantees and fast algorithms, A Tuning-free Robust and Efficient Approach to High-dimensional Regression, The landscape of empirical risk for nonconvex losses, I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error, Robust subset selection

Uses Software

• robustbase
• bootlib

Cites Work

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