Comparative Evaluation of Some Penalty Regression Techniques in the Multicollinearity Problem

This study aimed to predict an important biological trait, such as egg albumen height, in the presence of multicollinearity problem using some external quality parameters (egg weight, width, length, shape index, Haugh unit). Although a high coefficient of determination (R²=0.995) was obtained in the multiple regression model generated using the Classical Least Squares Method (LSM), serious multicollinearity problem was detected among the independent variables, negatively impacting the model's reliability. To address this issue, LASSO and Liu regression techniques were applied; in models generated with both methods, the explanatory factor R² decreased to approximately 89 %, but the multicollinearity problem was effectively mitigated. Comparisons also showed that the Liu regression model outperformed the LASSO model in terms of information criteria (AIC, cAIC, BIC). The results show that regression methods with penalty terms provide reliable and consistent estimates in data sets with multicollinearity problems, and these techniques are recommended for the analysis and modeling of biological data.