Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates
Machine Learning
2022-10-11 v3 Machine Learning
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.
Cite
@article{arxiv.2206.07594,
title = {Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates},
author = {Takeyuki Sasai},
journal= {arXiv preprint arXiv:2206.07594},
year = {2022}
}
Comments
Some mistakes are corrected, and one assumption is added to the main theorem