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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2505.07283 |
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| _version_ | 1866912370608046080 |
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| author | Snyman, Simon Han, Lengyi Braun, W. John |
| author_facet | Snyman, Simon Han, Lengyi Braun, W. John |
| contents | Data sharpening has been shown to reduce bias in nonparametric regression and density estimation. Its performance on nonlinear first order autoregressive models is studied theoretically and numerically in this paper. Although the asymptotic properties of data sharpening are not as favourable in the presence of serial dependence as in bivariate regression with independent responses, it is still found to reduce bias under mild conditions on the autoregression function. Numerical comparisons with the bias reduction method of Cheng et al. (2018) indicate that data sharpening is competitive in this setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Data Sharpening in Nonparametric Autoregressive Models Snyman, Simon Han, Lengyi Braun, W. John Methodology Data sharpening has been shown to reduce bias in nonparametric regression and density estimation. Its performance on nonlinear first order autoregressive models is studied theoretically and numerically in this paper. Although the asymptotic properties of data sharpening are not as favourable in the presence of serial dependence as in bivariate regression with independent responses, it is still found to reduce bias under mild conditions on the autoregression function. Numerical comparisons with the bias reduction method of Cheng et al. (2018) indicate that data sharpening is competitive in this setting. |
| title | On Data Sharpening in Nonparametric Autoregressive Models |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.07283 |