An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

Ozkale M. R., CAN F.

JOURNAL OF APPLIED STATISTICS, vol.44, no.12, pp.2251-2269, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 12
  • Publication Date: 2017
  • Doi Number: 10.1080/02664763.2016.1252732
  • Journal Name: JOURNAL OF APPLIED STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2251-2269
  • Keywords: Ridge regression, random effects, linear mixed model, variance modeling, mean-square error, MEAN SQUARED ERROR, REGRESSION, PREDICTION, PARAMETER
  • Çukurova University Affiliated: Yes

Abstract

This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.