线性回归模型中改进的几乎无偏岭估计

Enas Gawdat Yehia
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引用次数: 0

摘要

为了克服线性回归模型中解释变量之间多重共线性所带来的负面影响,本文提出了一种新的估计量,即改进的几乎无偏岭估计量,并给出了它的统计特性。此外,采用矩阵均方误差和偏差平方准则作为新估计量与普通最小二乘估计量、脊估计量和几乎无偏脊估计量进行比较的基础。进一步讨论了偏置参数的选择。此外,为了在标量均方误差的意义上检验新估计器与本文考虑的其他估计器的性能,进行了蒙特卡罗模拟研究和实际数据示例。结果表明,在标量均方误差方面,改进的几乎无偏脊估计器优于现有的估计器。因此,当线性回归模型中存在多重共线性时,可以安全地使用它。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Modified Almost Unbiased Ridge Estimator in Linear Regression Model
In order to overcome the negative effects caused by multicollinearity between the explanatory variables in the linear regression model, a new estimator namely modified almost unbiased ridge estimator is presented with its statistical characteristics in this paper. Also, the matrix mean squared error and squared bias criteria are adopted as a basis for comparisons between the new estimator and the ordinary least squares estimator, ridge estimator, and almost unbiased ridge estimator. Further, selection of the biasing parameter is discussed. Moreover, to check the performance of the new estimator versus the other estimators considered in this paper in the sense of scalar mean squared error, a study of Monte Carlo simulation and a real data example are conducted. The results indicate that in terms of scalar mean squared error, the new estimator, modified almost unbiased ridge estimator outperforms the others in use. So, it can be safely used when multicollinearity exists in a linear regression model.
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