Jackknife-After-Bootstrap as Logistic Regression Diagnostic Tool

BEYAZTAŞ U., ALIN A.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.43, no.9, pp.2047-2060, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 9
  • Publication Date: 2014
  • Doi Number: 10.1080/03610918.2013.783068
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2047-2060
  • Keywords: Jackknife, Diagnostics, Robustness, Masking, Logistic regression, Bootstrap, INFLUENTIAL OBSERVATIONS, IDENTIFICATION
  • Dokuz Eylül University Affiliated: Yes

Abstract

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.