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An evaluation of bootstrap methods for outlier detection in least squares regression

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  • Michael Martin
  • Steven Roberts

Abstract

Outlier detection is a critical part of data analysis, and the use of Studentized residuals from regression models fit using least squares is a very common approach to identifying discordant observations in linear regression problems. In this paper we propose a bootstrap approach to constructing critical points for use in outlier detection in the context of least-squares Studentized residuals, and find that this approach allows naturally for mild departures in model assumptions such as non-Normal error distributions. We illustrate our methodology through both a real data example and simulated data.

Suggested Citation

  • Michael Martin & Steven Roberts, 2006. "An evaluation of bootstrap methods for outlier detection in least squares regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(7), pages 703-720.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:703-720
    DOI: 10.1080/02664760600708863
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    References listed on IDEAS

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    1. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
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    Cited by:

    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.

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