# Ridge partial correlation screening for ultrahigh-dimensional data

arXiv: https://arxiv.org/abs/2504.19393 · HTML: https://mlsanity.com/paper/2504.19393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [High-dimensional Ordinary Least-squares Projection for Screening Variables](https://mlsanity.com/paper/1506.01782/) (2015)
- [Bayesian iterative screening in ultra-high dimensional linear regressions](https://mlsanity.com/paper/2107.10175/) (2021)
- [High-dimensional variable selection via tilting](https://mlsanity.com/paper/1611.08640/) (2016)
- [A note on marginal correlation based screening](https://mlsanity.com/paper/1707.08143/) (2017)

## Built on by (most cited first, 2 of 2)

- [Stability Selection via Variable Decorrelation](https://mlsanity.com/paper/2505.20864/) (2025)
- [A Stable Lasso](https://mlsanity.com/paper/2511.02306/) (2025)

## Abstract (arXiv)

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.