Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.09075 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913938712559616 |
|---|---|
| author | Padilla, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Chatterjee, Sabyasachi |
| author_facet | Padilla, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Chatterjee, Sabyasachi |
| contents | This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend filtering distributional regression, yielding convergence rates consistent with those for mean estimation. Furthermore, a general upper bound is derived for distributional regression under non-convex constraints, with a specific application to neural network-based estimators. Comprehensive experiments on both simulated and real data validate the theoretical contributions, demonstrating their practical effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk Bounds For Distributional Regression Padilla, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Chatterjee, Sabyasachi Machine Learning This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend filtering distributional regression, yielding convergence rates consistent with those for mean estimation. Furthermore, a general upper bound is derived for distributional regression under non-convex constraints, with a specific application to neural network-based estimators. Comprehensive experiments on both simulated and real data validate the theoretical contributions, demonstrating their practical effectiveness. |
| title | Risk Bounds For Distributional Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.09075 |