Principal components regression estimators under biased stochastic linear restrictions

KAÇIRANLAR S., ÖZBAY N., Polat M.

Journal of Statistical Computation and Simulation, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/00949655.2025.2501400
  • Dergi Adı: Journal of Statistical Computation and Simulation
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Biased stochastic linear restrictions, multicollinearity, principal components regression, weighted mixed estimator
  • Çukurova Üniversitesi Adresli: Evet

Özet

We propose new estimators to combat multicollinearity in linear regression model under biased stochastic linear restrictions. The new estimators are constructed by combining weighted mixed estimator and principal components regression estimator. Furthermore, necessary and sufficient conditions for the superiority of the new estimators over ordinary least squares estimator, principal components regression estimator and ridge estimator are derived in the sense of the mean square error criterion. Finally, a numerical example and a broad Monte Carlo simulation experiment are conducted to demonstrate the validation of theoretical results.