Adaptation of the jackknifed ridge methods to the linear mixed models

Abstract

The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models and to examine the superiorities, the linear combinations of the jackknifed ridge predictors over the ridge, principal components regression, r−k class and Henderson's predictors in terms of bias, covariance matrix and mean square error criteria. Numerical analyses are considered to illustrate the findings and a simulation study is conducted to see the performance of the jackknifed ridge predictors.

Keywords:

• Multicollinearity
• Henderson's predictors
• ridge predictors
• jackknifed ridge predictors
• linear mixed models

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

M. Revan Özkale http://orcid.org/0000-0001-7085-7403

Özge Kuran http://orcid.org/0000-0001-5632-001X

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 All 0.75, 0.80, 0.85, 0.90 values of ${\mathrm{P}\mathrm{T}\mathrm{V}}^{\ast }$ gave the same number of PCs.