New Method for Outlier Diagnostics in Linear Regression
Publication: Journal of Surveying Engineering
Volume 135, Issue 3
Abstract
The detection of the discordant points, i.e., outliers, in linear regression models is a problem, which has been studied extensively. Huber’s -estimation is recommended not only for robust regression but also for detecting outliers. However, -estimation does not show high performance in detecting outliers for some cases. The aim of this paper is to propose a new method for improving the ability of -estimation in outlier detection. It consists of the iterative combination of the -estimator along with a scheme of reducing weights in some observations at random. The theorems proving contribution of the proposed algorithms have also been included. A series of Monte Carlo simulation experiments show that the performance of the new algorithm in the presence of outliers is better than -estimation alone. By using the new method, the results, on average, improved by about 7%.
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Acknowledgments
The writers are very grateful to the editor and three anonymous reviewers for their valuable comments, which helped to improve the manuscript.
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Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 135 • Issue 3 • August 2009
Pages: 85 - 89
Copyright
© 2009 ASCE.
History
Received: Mar 5, 2008
Accepted: Sep 26, 2008
Published online: Jul 15, 2009
Published in print: Aug 2009
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