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

Ufuk Beyaztas and Aylin Alin ()

Statistical Papers, 2014, vol. 55, issue 4, 1018 pages

Abstract: 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. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Sufficient bootstrap; Jacknife; Bootstrap; Influential observation; Regression diagnostics (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00362-013-0548-4

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