Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models

Kuran O., Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.89, sa.1, ss.155-187, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 89 Sayı: 1
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/00949655.2018.1540622
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.155-187
  • Anahtar Kelimeler: Conditional Gauss discrepancy, conditional ridge, linear mixed model selection, multicollinearity, ridge regression, C-P
  • Çukurova Üniversitesi Adresli: Evet

Özet

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.