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A new stochastic restricted Liu-type estimator in linear regression model

机译:线性回归模型中的一种新的随机受限Liu型估计

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In this paper, we proposed an alternative stochastic restricted Liu-type estimator for the vector of parameters in a linear regression model. The new estimator is a combination of ordinary mixed estimator and Liu-Type estimator , which was proposed by Liu. Necessary and sufficient conditions for the superiority of new stochastic restricted Liu-type estimator over the ordinary mixed estimator and the Liu-type estimator in the mean squared error matrix sense are derived for the two cases in which the parametric restrictions are correct and are not correct. In particular, we showed that was superior in the mean squared error matrix sense over both and to the Liu-type estimator introduced by Liu (2003).
机译:在本文中,我们为线性回归模型中的参数向量提出了一种替代的随机受限Liu型估计器。新的估计量是由Liu提出的普通混合估计量和Liu型估计量的组合。对于参数限制正确和不正确的两种情况,推导了新的随机受限刘型估计量优于普通混合估计量和刘型估计量的均方误差矩阵意义的充要条件。 。特别是,我们证明了在均方误差矩阵意义上,它优于Liu(2003)引入的Liu型估计器。

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