Özkale, M. RevanKuran, Özge2024-03-192024-03-192022Özkale, M. R. ve Kuran, Ö. (2022). Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models. Concurrency and Computation: Practice and Experience, 34(28), 1-35.1532-0626https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.7366https://hdl.handle.net/11468/13640In this article, we characterize the mixed (Formula presented.) ((Formula presented.)) and conditional stochastic restricted ridge (Formula presented.) ((Formula presented.)) statistics that depend on the expected conditional Gauss discrepancy for the purpose of selecting the most appropriate model when stochastic restrictions are appeared in linear mixed models. Under the known and unknown variance components assumptions, we define two shapes of (Formula presented.) and (Formula presented.) statistics. Then, the article is concluded with both a Monte Carlo simulation study and a real data analysis, supporting the findings of the theoretical results on the (Formula presented.) and (Formula presented.) statistics.eninfo:eu-repo/semantics/closedAccessGauss discrepancyInformation criterionMallow's conceptual predictive statisticModel selectionRandom effectsArticle3428135WOS:0008650748000012-s2.0-8513941236710.1002/cpe.7366Q3Q3