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

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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.

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Acknowledgments

The authors are grateful to Michael Martin and an anonymous referee for their comments which improved the original version of the paper significantly. The authors also thank to “R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org”, for using R coding in the simulation study.

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Authors and Affiliations

  1. Department of Statistics, Dokuz Eylul University, Izmir, Turkey

    Ufuk Beyaztas & Aylin Alin

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  1. Ufuk Beyaztas
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  2. Aylin Alin
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Correspondence to Aylin Alin.

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Beyaztas, U., Alin, A. Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models. Stat Papers 55, 1001–1018 (2014). https://doi.org/10.1007/s00362-013-0548-4

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  • DOI: https://doi.org/10.1007/s00362-013-0548-4

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