Stochastic restricted Liu predictors in linear mixed models

ÖZKALE M. R., Kuran O.

Communications In Statistics-Simulation And Computation, vol.50, no.9, pp.2561-2580, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 9
  • Publication Date: 2021
  • Doi Number: 10.1080/03610918.2021.1957110
  • Journal Name: Communications In Statistics-Simulation And Computation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.2561-2580
  • Keywords: Generalized cross validation, linear mixed model, Liu predictor, stochastic restrictions, MEAN-SQUARE ERROR, RIDGE-REGRESSION, ESTIMATOR
  • Çukurova University Affiliated: Yes

Abstract

In this article, we propose the stochastic restricted Liu predictors by augmenting the stochastic restrictions to the linear mixed models. The Liu biasing parameter is selected via generalized cross validation (GCV) criterion. Comparisons between the stochastic restricted Liu estimators and several other estimators, namely the BLUE, the mixed and Liu estimators are made through the mean square error matrix criterion. Finally, a numerical example and a simulation study are done to show the performance of the estimators.