Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models
STATISTICAL PAPERS, cilt.55, sa.4, ss.1001-1018, 2014 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 55 Sayı: 4
- Basım Tarihi: 2014
- Doi Numarası: 10.1007/s00362-013-0548-4
- Dergi Adı: STATISTICAL PAPERS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.1001-1018
- Anahtar Kelimeler: Sufficient bootstrap, Jacknife, Bootstrap, Influential observation, Regression diagnostics
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
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.