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

06/15/2022

∙

by   Takeyuki Sasai, et al.

∙

0

∙

share

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, our estimation error bound is sharp.