Non-crossing convex quantile regression
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915537019207680 |
|---|---|
| author | Dai, Sheng Kuosmanen, Timo Zhou, Xun |
| author_facet | Dai, Sheng Kuosmanen, Timo Zhou, Xun |
| contents | Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while better maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_01371 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Non-crossing convex quantile regression Dai, Sheng Kuosmanen, Timo Zhou, Xun Methodology Applications Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while better maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem. |
| title | Non-crossing convex quantile regression |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2204.01371 |