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Activity Number: 532
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #308154
Title: On Masking and Swamping Robustness of Outlier Identifiers for Univariate Data
Author(s): Shanshan Wang*+ and Robert Serfling
Companies: and The University of Texas at Dallas
Keywords: outliers ; masking ; swamping ; nonparametric ; boxplot
Abstract:

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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