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Abstract Details
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Activity Number:
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176
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #306160 |
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Title:
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Probability of Concordance Monster (PoC-Mon) Devours Discordance on and off the Tau Path
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Author(s):
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Joseph Verducci*+ and Stephen Niklaus Bamattre
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Companies:
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The Ohio State University and The Ohio State University
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Address:
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404 Cockins Hall, Columbus, OH, 43210-1247, United States
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Keywords:
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concordance ;
Kendall's tau ;
copula ;
subpopulation ;
non-parametric ;
test
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Abstract:
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The geometry of the Tau Path suggests a faster way to sequentially eliminate discordant observations in order to discover a subpopulation in which variables are highly associated. Instead of following the (tau) path determined by the actual sample, PoC-mon devours observations based on where it expects them to be most discordant based on the null model of full independence in the population. PoC-mon has a computational advantage over the Tau Path method when applied to large samples, but there is a modest price in terms of loss of power against some alternative configurations of dependency in the subpopulation.
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