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This is a Quantitative Study study.

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

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Simulation study shows improved variable selection using robust reciprocal elastic net with lower mean absolute error and stability against outliers.

The robust reciprocal elastic net enhances variable selection accuracy, improving interpretability in high-dimensional settings.
Mean absolute error was significantly lower when applying the robust method compared to traditional models in simulations.
Simulation study assessed the method's performance under varying contamination levels to test robustness against outliers.
The approach may enable more reliable findings in practical applications where data anomalies are common.

Saif Hosam Raheem (2025) studied this question.

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Simulation study shows improved variable selection using robust reciprocal elastic net with lower mean absolute error and stability against outliers.