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Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model

机译:线性回归模型中Pitman贴近度准则下一些估计量的比较

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Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified r-k class estimator. They also showed that the modified r-k class estimator is superior to the ordinary least squares estimator and principal components regression estimator in the mean squared error matrix. In this paper, firstly, we will give a new method to obtain the modified r-k class estimator; secondly, we will discuss its properties in some detail, comparing the modified r-k class estimator to the ordinary least squares estimator and principal components regression estimator under the Pitman closeness criterion. A numerical example and a simulation study are given to illustrate our findings.
机译:Batah等。 (2009年)结合了无偏岭估计和主成分回归估计,并介绍了改进的r-k类估计。他们还表明,在均方误差矩阵中,改进的r-k类估计器优于普通最小二乘估计器和主成分回归估计器。在本文中,首先,我们将给出一种新的方法来获得改进的r-k类估计器。其次,我们将在修改后的r-k类估计量与普通最小二乘估计量和主分量回归估计量在Pitman接近度准则下进行比较的基础上,详细讨论其性质。数值例子和仿真研究说明了我们的发现。

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