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

Michael Martin and Steven Roberts

Journal of Applied Statistics, 2006, vol. 33, issue 7, 703-720

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.

Keywords: Case-based resampling; error distribution; externally Studentized residuals; internally Studentized residuals; jackknife-after-bootstrap; residual-based resampling; RSTUDENT (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1080/02664760600708863

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