Marginal ridge conceptual predictive model selection criterion in linear mixed models

Kuran O., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.50, sa.2, ss.581-607, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/03610918.2018.1563155
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.581-607
  • Anahtar Kelimeler: Linear mixed model selection, Marginal Gauss discrepancy, Marginal ridge Cp, Multicollinearity, Ridge regression, REGRESSION
  • Çukurova Üniversitesi Adresli: Evet

Özet

In linear mixed model selection under ridge regression, we propose the model selection criteria based on conceptual predictive () statistic.The first proposed criterion is marginal ridge C-p () statistic based on the expected marginal Gauss discrepancy. An improvement of MRCp (IMRCp) statistic is then suggested and demonstrated, which is also an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. Finally, a real data analysis and a Monte Carlo simulation study are given to examine the performance of the proposed criteria.