Robust methods for detecting bad leverage point in logistic regression

Gündoğan Aşık E., Altın Yavuz A., Küçük Z.

SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES, cilt.42, sa.5, ss.1344-1356, 2024 (ESCI)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 5
  • Basım Tarihi: 2024
  • Doi Numarası: 10.14744/sigma.2023.00108
  • Dergi Adı: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1344-1356
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

High-leverage points, known as good and bad leverage points, are also known as points away from center of x space. Bad leverage points are marginal values that show the incompatibility with misclassified observations and other observation values at x space. In the identification of bad leverage points, the problems of masking and swamping constitute a problem for the logistic regression model just as in the linear regression model. In this research, in addition to existing deviance components (DEVC), robust deviance components (RobDEVC) that are used to identify bad leverage points, different robust methods recommended to be used at the management of deviance components were examined. Also, for these methods, robust cut-off value combinations were examined as well. With the conducted simulation, robust methods recommended to be used in the deviance component method have shown better performance to identify bad leverage points by showing different cut-off values.