Akademik Veri Yönetim Sistemi
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.43, sa.9, ss.2047-2060, 2014 (SCI-Expanded)
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.