An integrated perspective of robustness in regression through the lens of the bias-variance trade-off
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909254511755264 |
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| author | Okuno, Akifumi |
| author_facet | Okuno, Akifumi |
| contents | This paper presents an integrated perspective on robustness in regression. Specifically, we examine the relationship between traditional outlier-resistant robust estimation and robust optimization, which focuses on parameter estimation resistant to imaginary dataset-perturbations. While both are commonly regarded as robust methods, these concepts demonstrate a bias-variance trade-off, indicating that they follow roughly converse strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An integrated perspective of robustness in regression through the lens of the bias-variance trade-off Okuno, Akifumi Methodology Machine Learning This paper presents an integrated perspective on robustness in regression. Specifically, we examine the relationship between traditional outlier-resistant robust estimation and robust optimization, which focuses on parameter estimation resistant to imaginary dataset-perturbations. While both are commonly regarded as robust methods, these concepts demonstrate a bias-variance trade-off, indicating that they follow roughly converse strategies. |
| title | An integrated perspective of robustness in regression through the lens of the bias-variance trade-off |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2407.10418 |