Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916767808356352 |
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| author | Di Gregorio, Simone Iafrate, Francesco |
| author_facet | Di Gregorio, Simone Iafrate, Francesco |
| contents | We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data. Results are shown for a simulated Itô-Taylor approximation of the sample paths. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration Di Gregorio, Simone Iafrate, Francesco Statistics Theory Machine Learning We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data. Results are shown for a simulated Itô-Taylor approximation of the sample paths. |
| title | Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2505.24383 |