Year: 2025
Type: article
Abstract: Variable selection in high-dimensional regression models is crucial for improving interpretability and predictive accuracy. Traditional penalized regression methods, such as the LASSO and Elastic Net, suffer from sensitivity to outliers, which can lead to biased coefficient estimation and incorrect variable selection. In this study, we propose a robust variable selection method based on the reciprocal elastic net penalty, which enhances sparsity while maintaining stability in the presence of extreme values. To further improve robustness, we integrate Huber loss and M-estimators, thereby mitigating the influence of outliers on the regression coefficients. The proposed method is evaluated through an extensive simulation study under different contamination levels and applied to a financial risk dataset, where the presence of anomalies is common. Performance is assessed using mean absolute error and breakdown point as evaluation criteria. The results demonstrate that the robust reciprocal elastic net outperforms traditional penalized regression models and provides more reliable variable selection in the presence of outliers.
DOI: 10.14419/m6v7vs95
Author: Saif Hosam Raheem
Institution: University of Al-Qadisiyah
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415554683
FWCI:
Cites:
Cited by:
Related to: