Jackknife-After-Bootstrap Method for Detection of Influential Observations in Linear Regression Models

BEYAZTAŞ U., ALIN A.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.42, no.6, pp.1256-1267, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 6
  • Publication Date: 2013
  • Doi Number: 10.1080/03610918.2012.661908
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1256-1267
  • Keywords: Bootstrap, Influential observation, Jackknife, Likelihood distance, Modified Cook's distance, t-star, Welsch's distance
  • Dokuz Eylül University Affiliated: Yes

Abstract

The jackknife-after-bootstrap (JaB) method has been proposed for detecting influential observations in linear regression models. The performance of JaB and the traditional methods have been compared for four different influence measures by designed simulation study and real world examples. Design includes different sample sizes and various modeling scenarios. The results reveal that proposed method is a good competitor or even better than traditional methods.