Two-parameter (TP) estimators are more advantageous to their one-parameter competitors since they have two biasing parameters that serve different purposes in linear regression model. At least one of these biasing parameters intends to gain a remedial impact for multicollinearity. Within this respect, we define a new TP estimator to eliminate the disorder originated from multicollinearity. Also, we perform theoretical comparisons for new TP estimator according to mean square error criterion. By minimizing the mean square error, we derive optimal estimators for both of the biasing parameters of this new estimator. Moreover, we recommend a mathematical programming approach to determine two biasing parameters, simultaneously. In this approach, we minimize the mean square error and improve the length of the newly defined TP estimator. In application part, computations regarding the estimations of the biasing parameters and mean square errors, and the length of the estimated coefficients are examined.

双参数 (TP) 估计器比单参数竞争对手更有利，因为它们有两个偏置参数，用于线性回归模型中的不同目的。这些偏置参数中的至少一个旨在对多重共线性产生补救影响。在这方面，我们定义了一个新的 TP 估计量来消除源自多重共线性的无序。此外，我们根据均方误差准则对新的 TP 估计器进行理论比较。通过最小化均方误差，我们为这个新估计器的两个偏置参数推导出最佳估计器。此外，我们推荐一种数学规划方法来同时确定两个偏置参数。在这种方法中，我们最小化了均方误差并改进了新定义的 TP 估计量的长度。