Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.89, no.1, pp.155-187, 2019 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 89 Issue: 1
- Publication Date: 2019
- Doi Number: 10.1080/00949655.2018.1540622
- Journal Name: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.155-187
- Keywords: Conditional Gauss discrepancy, conditional ridge, linear mixed model selection, multicollinearity, ridge regression, C-P
- Çukurova University Affiliated: Yes
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.