Robust Variable Selection Via Reciprocal Elastic Net in High-Dimensional Regression

  • Authors

    • Saif Hosam Raheem Department of Statistics, University of Al-Qadisiyah, College of Administration and Economics, Al-Qadisiyah, Iraq
    https://doi.org/10.14419/m6v7vs95
  • Robust Regression; Variable Selection; Reciprocal Elastic Net; Huber Loss; High-Dimensional Data; Financial Risk Analysis

  • 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.

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  • How to Cite

    Raheem, S. H. . . (2025). Robust Variable Selection Via Reciprocal Elastic Net in High-Dimensional Regression. International Journal of Advanced Mathematical Sciences, 11(2), 67-73. https://doi.org/10.14419/m6v7vs95