Improvement of mixed predictors in linear mixed models

Kuran O., Özkale M. R.

JOURNAL OF APPLIED STATISTICS, vol.48, no.5, pp.924-942, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1080/02664763.2020.1833182
  • Journal Name: JOURNAL OF APPLIED STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Veterinary Science Database, zbMATH
  • Page Numbers: pp.924-942
  • Keywords: Multicollinearity, mixed predictor, Liu predictor, stochastic-restricted Liu predictor, linear mixed model, LIU ESTIMATOR, RIDGE
  • Çukurova University Affiliated: Yes

Abstract

In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean square error matrix criterion. Finally, numerical examples and a simulation study are done to illustrate the findings. In numerical examples, we took some arbitrary observations from the data as the prior information since we did not have historical data or additional information about the data sets. The results show that this case does the new estimator gain efficiency over the constituent estimators and provide accurate estimation and prediction of the data.