Abstract Details
Activity Number:
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532
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #308154 |
Title:
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On Masking and Swamping Robustness of Outlier Identifiers for Univariate Data
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Author(s):
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Shanshan Wang*+ and Robert Serfling
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Companies:
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and The University of Texas at Dallas
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Keywords:
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outliers ;
masking ;
swamping ;
nonparametric ;
boxplot
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Abstract:
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Two key measures for studying robustness of outlier detection procedures are breakdown points associated with masking and swamping, respectively. Here masking and swamping breakdown points are evaluated for two leading outlier identifiers in the setting of univariate data: scaled deviation outlyingness, and centered rank outlyingness. Our findings shed new light on the comparison of (Median, MAD) versus (trimmed mean, trimmed standard deviation) in defining scaled deviation outlyingness. Also, our results explain how the boxplot acquires its robustness, namely through its use of outlyingness thresholds defined by its "fences" based on the interquartile range, rather than by specific upper and lower percentiles.
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Authors who are presenting talks have a * after their name.
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