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Activity Number: 656
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319076
Title: Dependence Discovery from Multimodal Data via Multiscale Graph Correlation
Author(s): Cencheng Shen* and Carey Priebe and Joshua Vogelstein and Mauro Maggioni
Companies: Temple University and The Johns Hopkins University and The Johns Hopkins University and Duke University
Keywords: distance correlation ; k-nearest-neighbor ; testing independence ; permutation test

Understanding and discovering dependence between multiple properties or measurements of our world is a fundamental task not just in science, but also policy, commerce, and other domains. We propose a novel dependence test statistic called ``Multiscale Graph Correlation'' (MGC), having the following properties: (1) Theoretical consistency such that the testing power converges to 1 under any dependency structure. (2) Strong empirical performance on a wide variety of low- and high-dimensional simulation settings. (3) Provides insight into the optimal local scale in which dependency is strongest. (4) On real data, detects dependence when it exists, and does not inflate the false positive rate in the absence of dependency. Briefly, we combine the ideas of distance correlation testing with nearest-neighbor testing to develop MGC, and demonstrate its properties and advantages by extensive theory, simulations, and real data examples. We can therefore use this test in a variety of settings in which previous tests failed to detect signal or provide insight.

Authors who are presenting talks have a * after their name.

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