WeSpeR: Computing non-linear shrinkage formulas for the weighted sample covariance
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918133162311680 |
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| author | Oriol, Benoit |
| author_facet | Oriol, Benoit |
| contents | We address the issue of computing the non-linear shrinkage formulas for the weighted sample covariance in high dimension. We use theoretical properties of the asymptotic sample spectrum in order to derive the \textit{WeSpeR} algorithm and significantly speed up non-linear shrinkage in dimension higher than $1000$. Empirical tests confirm the good properties of the \textit{WeSpeR} algorithm. We provide the implementation in PyTorch for it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14413 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WeSpeR: Computing non-linear shrinkage formulas for the weighted sample covariance Oriol, Benoit Statistics Theory Machine Learning Probability Computation We address the issue of computing the non-linear shrinkage formulas for the weighted sample covariance in high dimension. We use theoretical properties of the asymptotic sample spectrum in order to derive the \textit{WeSpeR} algorithm and significantly speed up non-linear shrinkage in dimension higher than $1000$. Empirical tests confirm the good properties of the \textit{WeSpeR} algorithm. We provide the implementation in PyTorch for it. |
| title | WeSpeR: Computing non-linear shrinkage formulas for the weighted sample covariance |
| topic | Statistics Theory Machine Learning Probability Computation |
| url | https://arxiv.org/abs/2410.14413 |