Nonparametric Bayesian estimation in a multidimensional diffusion model with high frequency data
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909731577135104 |
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| author | Hoffmann, Marc Ray, Kolyan |
| author_facet | Hoffmann, Marc Ray, Kolyan |
| contents | We consider nonparametric Bayesian inference in a multidimensional diffusion model with reflecting boundary conditions based on discrete high-frequency observations. We prove a general posterior contraction rate theorem in $L^2$-loss, which is applied to Gaussian priors. The resulting posteriors, as well as their posterior means, are shown to converge to the ground truth at the minimax optimal rate over Hölder smoothness classes in any dimension. Of independent interest and as part of our proofs, we show that certain frequentist penalized least squares estimators are also minimax optimal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_12267 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Nonparametric Bayesian estimation in a multidimensional diffusion model with high frequency data Hoffmann, Marc Ray, Kolyan Statistics Theory Probability 62G20, 62F15, 60J60 We consider nonparametric Bayesian inference in a multidimensional diffusion model with reflecting boundary conditions based on discrete high-frequency observations. We prove a general posterior contraction rate theorem in $L^2$-loss, which is applied to Gaussian priors. The resulting posteriors, as well as their posterior means, are shown to converge to the ground truth at the minimax optimal rate over Hölder smoothness classes in any dimension. Of independent interest and as part of our proofs, we show that certain frequentist penalized least squares estimators are also minimax optimal. |
| title | Nonparametric Bayesian estimation in a multidimensional diffusion model with high frequency data |
| topic | Statistics Theory Probability 62G20, 62F15, 60J60 |
| url | https://arxiv.org/abs/2211.12267 |