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A Modification of the Ridge Type Regression Estimators | Science Publications

机译:岭型回归估计量的一种修改|科学出版物

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> Problem statement: Many regression estimators have been used to remedy multicollinearity problem. The ridge estimator has been the most popular one. However, the obtained estimate is biased. Approach: In this stuyd, we introduce an alternative shrinkage estimator, called modified unbiased ridge (MUR) estimator for coping with multicollinearity problem. This estimator is obtained from Unbiased Ridge Regression (URR) in the same way that Ordinary Ridge Regression (ORR) is obtained from Ordinary Least Squares (OLS). Properties of MUR estimator are derived. Results: The empirical study indicated that the MUR estimator is more efficient and more reliable than other estimators based on Matrix Mean Squared Error (MMSE).Conclusion: In order to solve the multicollinearity problem, the MUR estimator was recommended.
机译: > 问题陈述:许多回归估计器已用于纠正多重共线性问题。岭估计器是最受欢迎的估计器。但是,获得的估计是有偏差的。 方法:在本研究中,我们介绍了一种替代收缩率估算器,称为修正无偏脊线(MUR)估算器,用于应对多重共线性问题。该估计量是从无偏岭回归(URR)获得的,与从普通最小二乘法(OLS)获得普通岭回归(ORR)的方法相同。推导了MUR估计器的属性。 结果:实证研究表明,基于矩阵均方误差(MMSE)的MUR估计器比其他估计器更有效,更可靠。结论:对于多重共线性问题,建议使用MUR估计器。

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