Jackknife-After-Bootstrap as Logistic Regression Diagnostic Tool


Beyaztas U., Alin A.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.43, ss.2047-2060, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 43 Konu: 9
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1080/03610918.2013.783068
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Sayfa Sayısı: ss.2047-2060

Özet

In this study, we propose using Jackknife-after-Bootstrap (JaB) method to detect influential observations in binary logistic regression model. Performance of the proposed method has been compared with the traditional method for standardized Pearson residuals, Cook's distance, change in the Pearson chi-square and change in the deviance statistics by both real world examples and simulation studies. The results reveal that under the various scenarios considered in this article, JaB performs better than the traditional method and is more robust to masking effect especially for Cook's distance.