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This is a Quantitative Study study.

Comparative Evaluation of Some Penalty Regression Techniques in the Multicollinearity Problem

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This analysis demonstrates that Liu and LASSO regression effectively handle multicollinearity, suggesting improvements for biological data modeling.

The Liu regression model outperformed LASSO, reducing multicollinearity issues while maintaining explanatory power.
High coefficient of determination was achieved using Classical Least Squares Method, with R² at 0.995 but with multicollinearity present.
Penalty regression techniques, including LASSO and Liu methods, provide reliable estimates despite multicollinearity challenges in data.
Recommendations include using penalty regression methods for better analysis, particularly in biological data contexts.

Tolga Tolun (2025) studied this question.

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This analysis demonstrates that Liu and LASSO regression effectively handle multicollinearity, suggesting improvements for biological data modeling.