OICC Press Mathematical Sciences 2251-7456 19 4 (December 2025) 2025 12 31 A Two-Parameter Ridge Estimators Approach to Mitigate Multicollinearity: Simulation and Application Results 10.57647/mathsci.2025.1904.16877 EN Muhammad Haseeb Department of Statistics, Quaid-i-Azam University Islamabad, Pakistan https://orcid.org/0009-0000-3401-0037 Muhammad Yousaf Shad Department of Statistics, Quaid-i-Azam University Islamabad, Pakistan Ali Rashash R Alzahrani Mathematics Department, Faculty of Sciences,Umm Al-Qura University, Makkah, Saudi Arabia https://orcid.org/0000-0002-0290-8843 Asma Ahmad Alzahrani Department of Mathematics, Faculty of Science, Al-Baha University, Al-Baha, Saudi Arabi Journal Article 2025 12 31 Multicollinearity in regression analysis arises when predictor variables are highly correlated, making it difficult to accurately estimate regression coefficients. This issue can distort model interpretations and inflate coefficient variances, making estimates sensitive to small data changes. To address this issue, several ridge estimators have been developed in the past to reduce the effect of multicollinearity and improve the model stability. To overcome the negative affect of multicollinearity, we introduce three newly proposed two-parameter ridge estimators, named HITPR1, HITPR2, and HITPR3, which dynamically adjust the ridge parameter for different multicollinearity structures. We evaluate the performance of these proposed estimators through a comprehensive simulation study and employing Mean Squared Error (MSE) criterion. The numerical results show that HITPR1 estimator performs better with higher efficiency and lower MSE, outperforming the other estimators in different settings. To further investigate the performance and applicability of the newly proposed estimators, two real-world datasets, have been utilized.