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This is a Quantitative Study study.

October 11, 2025

A Modified Correlation Based Regularization Technique for Regression Estimation and Feature Selection

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This research proposes the CL1CP method for accurate variable selection, highlighting advantages over LASSO and elastic-net.

The CL1CP method reduces bias by using derived scaling transformations based on predictor correlations.
Simulation studies show that CL1CP outperforms LASSO and elastic-net in sparse model selection.
The proposed technique enhances estimation and prediction accuracy for grouped-variable selection.
Real-life data applications support the advantages of CL1CP in variable selection compared to existing methods.

Adeniyia et al. (2025) studied this question.

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