This article introduces an iterative restricted OK estimator in generalized linear models to address the dilemma of multicollinearity by imposing exact linear restrictions on the parameters. It is a versatile estimator, which contains maximum likelihood (ML), restricted ML, Liu, restricted Liu, ridge and restricted ridge estimators in generalized linear models. To figure out the performance of restricted OK estimator over its counterparts, various comparisons are given where the performance evaluation criterion is the scalar mean square error (SMSE). Thus, illustrations and simulation studies for Gamma and Poisson responses are conducted apart from theoretical comparisons to see the performance of the estimators in terms of estimated and predicted MSE. Besides, the optimization techniques are applied to find the values of tuning parameters by minimizing SMSE and by using genetic algorithm.

本文介绍了广义线性模型中的迭代受限 OK 估计器，通过对参数施加精确的线性限制来解决多重共线性的困境。它是一种通用估计器，包含广义线性模型中的最大似然 (ML)、受限 ML、Liu、受限 Liu、岭和受限岭估计器。为了弄清楚受限 OK 估计器相对于其对应物的性能，给出了各种比较，其中性能评估标准是标量均方误差 (SMSE)。因此，除了理论比较之外，还对 Gamma 和 Poisson 响应进行了图解和模拟研究，以查看估计器在估计和预测 MSE 方面的性能。除了，