Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
Machine Learning
2024-08-05 v1 Machine Learning
Abstract
We investigate a problem estimating coefficients of linear regression under sparsity assumption when covariates and noises are sampled from heavy tailed distributions. Additionally, we consider the situation where not only covariates and noises are sampled from heavy tailed distributions but also contaminated by outliers. Our estimators can be computed efficiently, and exhibit sharp error bounds.
Keywords
Cite
@article{arxiv.2408.01336,
title = {Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers},
author = {Takeyuki Sasai and Hironori Fujisawa},
journal= {arXiv preprint arXiv:2408.01336},
year = {2024}
}
Comments
This research builds on and improves the results of arxiv:2206.07594. There will be no further update for the earlier manuscript