Ridge partial correlation screening for ultrahigh-dimensional data
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913810251513856 |
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| author | Wang, Run Nguyen, An Dutta, Somak Roy, Vivekananda |
| author_facet | Wang, Run Nguyen, An Dutta, Somak Roy, Vivekananda |
| contents | Variable selection in ultrahigh-dimensional linear regression is
challenging due to its high computational cost. Therefore, a
screening step is usually conducted before variable selection to
significantly reduce the dimension. Here we propose a novel and
simple screening method based on ordering the absolute sample ridge
partial correlations. The proposed method takes into account not
only the ridge regularized estimates of the regression coefficients
but also the ridge regularized partial variances of the predictor
variables providing sure screening property without strong
assumptions on the marginal correlations. Simulation study and a
real data analysis show that the proposed method has a competitive
performance compared with the existing screening procedures. A
publicly available software implementing the proposed screening
accompanies the article. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ridge partial correlation screening for ultrahigh-dimensional data Wang, Run Nguyen, An Dutta, Somak Roy, Vivekananda Methodology Machine Learning Variable selection in ultrahigh-dimensional linear regression is challenging due to its high computational cost. Therefore, a screening step is usually conducted before variable selection to significantly reduce the dimension. Here we propose a novel and simple screening method based on ordering the absolute sample ridge partial correlations. The proposed method takes into account not only the ridge regularized estimates of the regression coefficients but also the ridge regularized partial variances of the predictor variables providing sure screening property without strong assumptions on the marginal correlations. Simulation study and a real data analysis show that the proposed method has a competitive performance compared with the existing screening procedures. A publicly available software implementing the proposed screening accompanies the article. |
| title | Ridge partial correlation screening for ultrahigh-dimensional data |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2504.19393 |