Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models

Özkale M. R., Kuran O.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, cilt.34, sa.28, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 28
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/cpe.7366
  • Dergi Adı: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Gauss discrepancy, information criterion, Mallow's conceptual predictive statistic, model selection, random effects, REGRESSION
  • Çukurova Üniversitesi Adresli: Evet

Özet

In this article, we characterize the mixed Cp$$ {C}_p $$ (CMCp$$ {\mathrm{CMC}}_p $$) and conditional stochastic restricted ridge Cp$$ {C}_p $$ (CSRRCp$$ {\mathrm{CSRRC}}_p $$) 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 CMCp$$ {\mathrm{CMC}}_p $$ and CSRRCp$$ {\mathrm{CSRRC}}_p $$ 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 CMCp$$ {\mathrm{CMC}}_p $$ and CSRRCp$$ {\mathrm{CSRRC}}_p $$ statistics.