Lecture Notes on High Dimensional Linear Regression
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917122844655616 |
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| author | Quaini, Alberto |
| author_facet | Quaini, Alberto |
| contents | These lecture notes cover advanced topics in linear regression, with an in-depth exploration of the existence, uniqueness, relations, computation, and non-asymptotic properties of the most prominent estimators in this setting. The covered estimators include least squares, ridgeless, ridge, and lasso. The content follows a proposition-proof structure, making it suitable for students seeking a formal and rigorous understanding of the statistical theory underlying machine learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lecture Notes on High Dimensional Linear Regression Quaini, Alberto Methodology Computation Machine Learning These lecture notes cover advanced topics in linear regression, with an in-depth exploration of the existence, uniqueness, relations, computation, and non-asymptotic properties of the most prominent estimators in this setting. The covered estimators include least squares, ridgeless, ridge, and lasso. The content follows a proposition-proof structure, making it suitable for students seeking a formal and rigorous understanding of the statistical theory underlying machine learning methods. |
| title | Lecture Notes on High Dimensional Linear Regression |
| topic | Methodology Computation Machine Learning |
| url | https://arxiv.org/abs/2412.15633 |