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

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  • Ufuk Beyaztas
  • Aylin Alin

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

Suggested Citation

  • Ufuk Beyaztas & Aylin Alin, 2014. "Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models," Statistical Papers, Springer, vol. 55(4), pages 1001-1018, November.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:4:p:1001-1018
    DOI: 10.1007/s00362-013-0548-4
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    References listed on IDEAS

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    1. Michael Martin & Steven Roberts, 2010. "Jackknife-after-bootstrap regression influence diagnostics," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 257-269.
    2. Whasoo Bae & Soonyoung Hwang & Choongrak Kim, 2008. "Influence diagnostics in the varying coefficient model with longitudinal data," Computational Statistics, Springer, vol. 23(2), pages 185-196, April.
    3. Singh, Sarjinder & Sedory, Stephen A., 2011. "Sufficient bootstrapping," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1629-1637, April.
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    Cited by:

    1. Ufuk Beyaztas & Beste H. Beyaztas, 2019. "On Jackknife-After-Bootstrap Method for Dependent Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1613-1632, April.

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