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More on Liu-Type Estimator in Linear Regression

Authors: Kejian Liu;

More on Liu-Type Estimator in Linear Regression

Abstract

Abstract Recently, Liu [Liu, K. (2003). Using Liu-type estimator to combat collinearity. Commun. Statist. Theory Methods 32:1009–1020] introduced the Liu-type estimator to combat collinearity in linear regression. The Liu-type estimator can be applied in two ways. First, when the effect of collinearity is moderate, the Liu-type estimator can be used as a means to further improve the performance of ridge regression. In this case, k selected by existing methods for ridge regression usually is random. Here, we generalize the results in Liu (2003) to the random k case and prove that when k is selected by Hoerl–Kennard formula, the Liu-type estimator is still able to further improve ridge regression. Second, when collinearity is severe, we suggest to choose k based on condition number consideration, which is different from the existing selection methods, and then use the second parameter in Liu-type estimator to make adjustment. We use simulation to provide some empirical justification for the choice of k prop...

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Average
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