Robust Regression via Mutivariate Regression Depth

Chao Gao

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arXiv · 1702.04656

Robust Regression via Mutivariate Regression Depth

Chao Gao

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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.

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Chao Gao. 2017-02-15. Robust Regression via Mutivariate Regression Depth. https://arxiv.org/abs/1702.04656

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