Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates

Abstract

Robust and sparse estimation of linear regression coefficients is investigated. The situation addressed by the present paper is that covariates and noises are sampled from heavy-tailed distributions, and the covariates and noises are contaminated by malicious outliers. Our estimator can be computed efficiently. Further, the error bound of the estimator is nearly optimal.

Some mistakes are corrected, and one assumption is added to the main theorem