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 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 9
  • Publication Date: 2021
  • Doi Number: 10.1080/03610918.2021.1957110
  • Title of Journal : Communications In Statistics-Simulation And Computation
  • Page Numbers: pp.2561-2580
  • Keywords: Generalized cross validation, linear mixed model, Liu predictor, stochastic restrictions, MEAN-SQUARE ERROR, RIDGE-REGRESSION, ESTIMATOR

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.