Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911975295942656 |
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| author | Sasai, Takeyuki Fujisawa, Hironori |
| author_facet | Sasai, Takeyuki Fujisawa, Hironori |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers Sasai, Takeyuki Fujisawa, Hironori Machine Learning 62J07 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. |
| title | Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers |
| topic | Machine Learning 62J07 |
| url | https://arxiv.org/abs/2408.01336 |