Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models

Beyaztas U. , Alin A.

STATISTICAL PAPERS, cilt.55, sa.4, ss.1001-1018, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 55 Konu: 4
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00362-013-0548-4
  • Sayfa Sayıları: ss.1001-1018


In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The performances of the sufficient and conventional JaB methods have been compared for detecting influential observations in linear regression. Comparison is based on two real-world examples and an extensive designed simulation study. Design includes different sample sizes and various modeling scenarios. The results reveal that proposed method is a good competitor for conventional JaB method with less standard error and amount of computation.