Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
John Wiley and Sons Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Gauss discrepancy, Information criterion, Mallow's conceptual predictive statistic, Model selection, Random effects
Kaynak
Concurrency and Computation: Practice and Experience
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
34
Sayı
28
Künye
Özkale, 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.
