An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression
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]
|Collections||ANU Research Publications|
|Source:||Journal of Applied Statistics|
|01_Martin_An_Evaluation_of_Bootstrap_2006.pdf||136.65 kB||Adobe PDF||Request a copy|
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