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Activity Number: 546 - Foundational Issues in Machine Learning
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #309655
Title: BET on Independence
Author(s): Kai Zhang*
Companies: UNC Chapel Hill
Keywords: Nonparametric Inference; Multiple Testing; Binary Expansion; Contingency Table; Hadamard Transform

We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a novel binary expansion filtration approximation of the copula. Through a Hadamard transform, we find that the symmetry statistics in the filtration are complete sufficient statistics for dependence. These statistics are also uncorrelated under the null. By utilizing symmetry statistics, the BET avoids the problem of non-uniform consistency and improves upon a wide class of commonly used methods (a) by achieving the minimax rate in sample size requirement for reliable power and (b) by providing clear interpretations of global relationships upon rejection of independence. The binary expansion approach also connects the symmetry statistics with the current computing system to facilitate efficient bitwise implementation. We illustrate the advantages of the BET with through simulated and real data.

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

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