Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models


Kuran O. , Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.89, no.1, pp.155-187, 2019 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 89 Issue: 1
  • Publication Date: 2019
  • Doi Number: 10.1080/00949655.2018.1540622
  • Title of Journal : JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Page Numbers: pp.155-187

Abstract

In this paper, we focus on the progress of variant of conceptual predictive () statistic and we propose the model selection criterion that depend on statistic under ridge regression for linear mixed model selection. The proposed criterion is conditional ridge () statistic based on the expected conditional Gauss discrepancy. Two versions of statistic under the assumptions that the variance components are known and unknown are derived. To examine the performance of the proposed criterion, a real data analysis and a Monte Carlo simulation study are given.