Inference on multiple quantiles in regression models by a rank-score approach
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908948544618496 |
|---|---|
| author | De Santis, Riccardo Vesely, Anna Andreella, Angela |
| author_facet | De Santis, Riccardo Vesely, Anna Andreella, Angela |
| contents | This paper tackles the challenge of performing multiple quantile regressions across different quantile levels and the associated problem of controlling the familywise error rate, an issue that is generally overlooked in practice. We propose a multivariate extension of the rank-score test and embed it within a closed-testing procedure to efficiently account for multiple testing. Then we further generalize the multivariate test to enhance statistical power against alternatives in selected directions. Theoretical foundations and simulation studies demonstrate that our method effectively controls the familywise error rate while achieving higher power than traditional corrections, such as Bonferroni. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference on multiple quantiles in regression models by a rank-score approach De Santis, Riccardo Vesely, Anna Andreella, Angela Methodology This paper tackles the challenge of performing multiple quantile regressions across different quantile levels and the associated problem of controlling the familywise error rate, an issue that is generally overlooked in practice. We propose a multivariate extension of the rank-score test and embed it within a closed-testing procedure to efficiently account for multiple testing. Then we further generalize the multivariate test to enhance statistical power against alternatives in selected directions. Theoretical foundations and simulation studies demonstrate that our method effectively controls the familywise error rate while achieving higher power than traditional corrections, such as Bonferroni. |
| title | Inference on multiple quantiles in regression models by a rank-score approach |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.07999 |