Partial heteroscedastic deconvolution estimation in nonparametric regression
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917227151753216 |
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| author | Thiam, Baba |
| author_facet | Thiam, Baba |
| contents | In this paper, we consider a partial deconvolution kernel estimator for nonparametric regression when some covariates are measured with error while others are observed without error. We focus on a general and realistic setting in which the measurement errors are heteroscedastic. We propose a kernel-based estimator of the regression function in this framework and show that it achieves the optimal convergence rate under suitable regularity conditions. The finite-sample performance of the proposed estimator is illustrated through simulation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20341 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Partial heteroscedastic deconvolution estimation in nonparametric regression Thiam, Baba Statistics Theory In this paper, we consider a partial deconvolution kernel estimator for nonparametric regression when some covariates are measured with error while others are observed without error. We focus on a general and realistic setting in which the measurement errors are heteroscedastic. We propose a kernel-based estimator of the regression function in this framework and show that it achieves the optimal convergence rate under suitable regularity conditions. The finite-sample performance of the proposed estimator is illustrated through simulation studies. |
| title | Partial heteroscedastic deconvolution estimation in nonparametric regression |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2601.20341 |