Influence diagnostics in geographically weighted ridge regression

TÜRKAN S.

Communications in Statistics: Simulation and Computation, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/03610918.2023.2261078
  • 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: Cook’s distance, Multicollinearity, Penalized geographically weighted regression, Pena’s statistics
  • Hacettepe Üniversitesi Adresli: Evet

Özet

The presence of influential observations affect the accuracy of the model results. Similarly, the multicollinearity is the other problem that affect accuracy. Literature has shown that both the presence of influential observations and multicollinearity can arise in the model at the same time. This situation is also encountered in the geographically weighted regression. This study introduces the case-deletion diagnostics for the ridge estimator in geographically weighted regression model. To identify the influential observations, Cook’s Distance and Pena’s Statistics are adapted for the geographically weighted ridge regression model. The proposed diagnostics have been applied through a well-known real data. The influential observations have been detected by Cook’s Distance and Pena’s Statistics plots in real data. In addition, the simulated data has been used to reveal the performance of the proposed diagnostics. The results of the simulation showed that Pena’s Statistics has been very good at detecting high leverage outliers in the large data sets, especially.