2012
DOI: 10.1080/03610918.2011.600503
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Computational Method for Jackknifed Generalized Ridge Tuning Parameter based on Generalized Maximum Entropy

Abstract: In this article, a new method to estimate the Jackknifed generalized ridge tuning parameter, based on the Jackknifed Ridge-trace and an analytical method borrowed from generalized maximum entropy, is presented. The ideas in the article are illustrated and evaluated using to the well-known Portland cement data set and simulations.

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Cited by 2 publications
(4 citation statements)
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“…The improvement accomplished in this paper overcomes this weakness of the original Ridge-GME parameter estimator. The simulation study and the empirical applications discussed in this paper, as well as the results obtained by Macedo et al (2010) and Erdugan and Akdeniz (2012), reveal a good performance of this Ridge Regression procedure in the case of regression models with small samples sizes affected by collinearity.…”
Section: Discussionsupporting
confidence: 53%
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“…The improvement accomplished in this paper overcomes this weakness of the original Ridge-GME parameter estimator. The simulation study and the empirical applications discussed in this paper, as well as the results obtained by Macedo et al (2010) and Erdugan and Akdeniz (2012), reveal a good performance of this Ridge Regression procedure in the case of regression models with small samples sizes affected by collinearity.…”
Section: Discussionsupporting
confidence: 53%
“…The Ridge-GME parameter estimator was introduced by Macedo et al (2010) and was adapted for a jackknife procedure by Erdugan and Akdeniz (2012). The basic idea underlying the Ridge-GME parameter estimator is to combine the ridge trace and the GME estimator.…”
Section: Ridge Regression and Generalized Maximum Entropymentioning
confidence: 99%
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