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An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression

Martin, Michael; Roberts, Steven


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...[Show more]

CollectionsANU Research Publications
Date published: 2006
Type: Journal article
Source: Journal of Applied Statistics
DOI: 10.1080/02664760600708863


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