Analysis of a multi-target linear shrinkage covariance estimator
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915193155485696 |
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| author | Oriol, Benoit |
| author_facet | Oriol, Benoit |
| contents | Multi-target linear shrinkage is an extension of the standard single-target linear shrinkage for covariance estimation. We combine several constant matrices - the targets - with the sample covariance matrix. We derive the oracle and a \textit{bona fide} multi-target linear shrinkage estimator with exact and empirical mean. In both settings, we proved its convergence towards the oracle under Kolmogorov asymptotics. Finally, we show empirically that it outperforms other standard estimators in various situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20086 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of a multi-target linear shrinkage covariance estimator Oriol, Benoit Statistics Theory Machine Learning Probability Multi-target linear shrinkage is an extension of the standard single-target linear shrinkage for covariance estimation. We combine several constant matrices - the targets - with the sample covariance matrix. We derive the oracle and a \textit{bona fide} multi-target linear shrinkage estimator with exact and empirical mean. In both settings, we proved its convergence towards the oracle under Kolmogorov asymptotics. Finally, we show empirically that it outperforms other standard estimators in various situations. |
| title | Analysis of a multi-target linear shrinkage covariance estimator |
| topic | Statistics Theory Machine Learning Probability |
| url | https://arxiv.org/abs/2405.20086 |