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Activity Number:
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709
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2016 : 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 #318773
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Title:
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A Cluster-Based Outlier Detection Scheme for Multivariate Data
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Author(s):
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J. Marcus Jobe* and Michael Pokojovy
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Companies:
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Miami University of Ohio (Retired) and Karlsruhe Institute of Technology
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Keywords:
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Size and power ;
Swamping rate ;
Protected F-test
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Abstract:
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Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate data set of interest. A computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw's minimum covariance determinant method with a multi-step cluster-based algorithm is considered. This method initially filters out potential masking points. Simulation studies show that our new method is frequently better for outlier detection compared to the most robust procedure. Application of our proposed method to real data is compared to that of a leading robust approach.
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Authors who are presenting talks have a * after their name.
