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Comparisons of the r ? k class estimator to the ordinary least squares estimator under the Pitman’s closeness criterion

机译:r的比较Pitman贴近度准则下的k类估计量到普通最小二乘估计量

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摘要

In the presence of multicollinearity, the r ? k class estimator is proposed as an alternative to the ordinary least squares (OLS) estimator which is a general estimator including the ordinary ridge regression (ORR), the principal components regression (PCR) and the OLS estimators. Comparison of competing estimators of a parameter in the sense of mean square error (MSE) criterion is of central interest. An alternative criterion to the MSE criterion is the Pitman’s (1937) closeness (PC) criterion. In this paper, we compare the r ? k class estimator to the OLS estimator in terms of PC criterion so that we can get the comparison of the ORR estimator to the OLS estimator under the PC criterion which was done by Mason et al. (1990) and also the comparison of the PCR estimator to the OLS estimator by means of the PC criterion which was done by Lin and Wei (2002).
机译:在存在多重共线性的情况下,提出了k类估计器作为普通最小二乘(OLS)估计器的替代,普通最小二乘估计器是包括普通岭回归(ORR),主成分回归(PCR)和OLS估计器的常规估计器。从均方误差(MSE)准则的意义上比较参数的竞争估计量是我们关注的重点。 MSE准则的替代准则是Pitman(1937)接近度(PC)准则。在本文中,我们比较了根据PC准则,将k类估计器与OLS估计器进行比较,这样我们就可以在由Mason等人完成的PC准则下获得ORR估计器与OLS估计器的比较。 (1990年),并通过PC准则将PCR估计量与OLS估计量进行比较,这是由Lin和Wei(2002)完成的。

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