Adaptation of the jackknifed ridge methods to the linear mixed models

Ozkale M. R., Kuran O.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.89, no.18, pp.3413-3452, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 89 Issue: 18
  • Publication Date: 2019
  • Doi Number: 10.1080/00949655.2019.1669037
  • Journal Name: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3413-3452
  • Keywords: Multicollinearity, Henderson's predictors, ridge predictors, jackknifed ridge predictors, linear mixed models, REGRESSION, ESTIMATOR, PREDICTIONS, ERROR
  • Çukurova University Affiliated: Yes

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.