Adjustive Liu-Type Estimators in Linear Regression Models

摘要

In this article, we aim to put forward the notion of adjustive Liu-type estimator (ALTE) in the linear regression model. First, the explicit expression of the optimal selection of the adjustive factors is derived under the PRESS criterion through matrix techniques. Then, the results are applied to the dataset on Portland cement. Moreover, to select biasing parameters from the theoretical point of view, we extend ALTE to the generalized version (GALTE) and obtained the optimal ones. The results of the Portland cement data show that ALTE's and GALTE's can substantially improve the ordinary least squares estimator and Liu-type estimators.