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Activity Number: 176
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306003
Title: Tau Path Test for Subpopulation Dependency: Basics, Extensions, and Examples
Author(s): Stephen Niklaus Bamattre*+ and Joseph Verducci
Companies: The Ohio State University and The Ohio State University
Address: Department of Statistics, Columbus, OH, 43210, United States
Keywords: data mining ; machine learning ; nonparametrics ; copulas ; combinatorics

The tau-path is a technique to detect monotone association between a pair of variables in an unspecified population. Extracting this dependency structure can be reduced to deconvolving a mixture of two absolutely continuous distributions. Alternative approaches perform this deconvolution for monotone dependence but rely on a fixed subpopulation. The rejection bounds for detection of an associated subpopulation, previously estimated via simulation, are obtained from the underlying geometry of the association and related to the asymptotic distribution of the Longest Increasing Subsequence (LIS) of a random permutation. This allows for the procedure to be applied to large n data, where sufficient characterization of the tau-path on the basis of generated random permutations is computationally intractable. We demonstrate that this approach detects substantial relationships embedded within a marketing data set provided by Nationwide Insurance.

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