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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.21505 |
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| _version_ | 1866915581522870272 |
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| author | Nakakita, Shogo |
| author_facet | Nakakita, Shogo |
| contents | We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of i.i.d. paths. In particular, we show that Lasso and Slope estimators can achieve the minimax optimal rate of convergence. We exhibit numerical experiments for sparse estimation methods and show their performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse estimation for the drift of high-dimensional Ornstein--Uhlenbeck processes with i.i.d. paths Nakakita, Shogo Statistics Theory We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of i.i.d. paths. In particular, we show that Lasso and Slope estimators can achieve the minimax optimal rate of convergence. We exhibit numerical experiments for sparse estimation methods and show their performance. |
| title | Sparse estimation for the drift of high-dimensional Ornstein--Uhlenbeck processes with i.i.d. paths |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2510.21505 |