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.
"Robust regression via mutivariate regression depth." Bernoulli 26 (2) 1139 - 1170, May 2020. https://doi.org/10.3150/19-BEJ1144