Robust Regression via Mutivariate Regression Depth

Abstract

This paper studies robust regression in the settings of Huber's $\epsilon$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $\epsilon$-contamination models for various regression problems including nonparametric regression, sparse linear regression, reduced rank regression, etc. We also discuss a general notion of depth function for linear operators that has potential applications in robust functional linear regression.

References in corpus (2)

• Robust Estimation of High-Dimensional Mean Regression
• Variance Breakdown of Huber (M)-estimators: $n/p\to m\in (1,\infty )$