| Autor: |
Joudah, Ibrahim, Muller, Samuel, Zhu, Houying |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Handling high-dimensional datasets presents substantial computational challenges, particularly when the number of features far exceeds the number of observations and when features are highly correlated. A modern approach to mitigate these issues is feature screening. In this work, the High-dimensional Ordinary Least-squares Projection (HOLP) feature screening method is advanced by employing adaptive ridge regularization. The impact of the ridge penalty on the Ridge-HOLP method is examined and Air-HOLP is proposed, a data-adaptive advance to Ridge-HOLP where the ridge-regularization parameter is selected iteratively and optimally for better feature screening performance. The proposed method addresses the challenges of penalty selection in high dimensions by offering a computationally efficient and stable alternative to traditional methods like bootstrapping and cross-validation. Air-HOLP is evaluated using simulated data and a prostate cancer genetic dataset. The empirical results demonstrate that Air-HOLP has improved performance over a large range of simulation settings. We provide R codes implementing the Air-HOLP feature screening method and integrating it into existing feature screening methods that utilize the HOLP formula. |
| Databáze: |
arXiv |
| Externí odkaz: |
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