Abstract #300734

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JSM 2003 Abstract #300734
Activity Number: 254
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 12:00 PM to 1:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300734
Title: A Comparison of Recent Multiple Outlier Detection Methods for Regression Data
Author(s): Ali S. Hadi*+ and Nedret Billor and Gulsen Kiral
Companies: Cornell University and University of Iowa and Cukirova University
Address: 809 United Nations Plaza, New York, NY, 10017-3503,
Keywords: robust methods ; forward search ; backward search ; outlier detection ; leverage points ; masking
Abstract:

There has been considerable interest in recent years in the detection and accommodation of multiple outliers in regression type of data. There are two broad classes of multiple outlier detection methods: direct methods and indirect methods. We compare multiple outlier detection methods for linear regression from these classes that are either most recently published or most frequently cited in the statistical literature by using an extensive simulation. The Monte Carlo simulation is used to determine the performance of the multiple outlier detection methods for a wide variety of cases depending on the outlier configurations: (a) interior X-space regression outliers, (b) exterior X-space regression outliers. The results for each procedure's performance depending on a wide variety of realistic and challenging regression scenarios and recommendations are given.

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