ABSTRACT

A large and wide variety of ridge parameter estimators proposed for linear regression models exist in the literature. Actually proposing new ridge parameter estimator lately proving its efficiency on few cases seems endless. However, so far there is no ridge parameter estimator that can serve best for any sample size or any degree of collinearity among regressors. In this study we propose a new robust ridge parameter estimator that serves best for any case assuring that is free of sample size, number of regressors and degree of collinearity. This is in fact realized by choosing three best from enormous number of ridge parameter estimators performing well in different cases in developing the new ridge parameter estimator in a way of search method providing the smallest mean square error values of regression parameters. After that a simulation study is conducted to show that the proposed parameter is robust. In conclusion, it is found that this ridge parameter estimator is promising in any case. Moreover, a recent data set is used as an example for illustration to show that the proposed ridge parameter estimator is performing better.

摘要

文献中存在大量针对线性回归模型提出的岭参数估计器。实际上，最近提出新的岭参数估计器证明其在少数情况下的效率似乎是无穷无尽的。然而，到目前为止，还没有岭参数估计器可以为任何样本量或回归变量之间的任何共线性程度提供最佳服务。在这项研究中，我们提出了一种新的稳健的岭参数估计器，它最适合任何情况，确保没有样本量、回归量和共线性程度。这实际上是通过在以提供回归参数的最小均方误差值的搜索方法的方式开发新的脊参数估计器时从大量在不同情况下表现良好的脊参数估计器中选择三个最佳来实现的。之后进行模拟研究以表明所提出的参数是稳健的。总之，发现这种岭参数估计器在任何情况下都是有希望的。此外，以最近的数据集为例进行说明，以表明所提出的岭参数估计器的性能更好。