A hybridized consistent Akaike type information criterion for regression models in the presence of multicollinearity

Dünder E.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/03610918.2023.2169710
  • 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
  • Anahtar Kelimeler: Information criteria, Model selection, Regression modeling, SELECTION, COMPLEXITY
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Consistent Akaike information criterion (CAIC) is an adjusted form of classical AIC. This criterion was developed by modifying the penalty. As a result, we propose a novel AIC type criterion, called CAIC (n alpha). The proposed criterion includes a dynamic parameter for controlling the penalty further. The distinctive feature of CAIC (n alpha) is to penalize multicollinearity level considering the information complexity measures. CAIC (n alpha) requires the alpha parameter, and in addition, a procedure is proposed to estimate alpha based on the information complexity of the regression model. Monte Carlo simulations and real data set examples demonstrate that CAIC (n alpha) performs better than classical information criteria for the potential multicollinearity problems.