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Activity Number: 288 - New Insights from Classical Wisdom—honoring Lawrence D. Brown’s Contributions to Graduate Student Education
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #304959
Title: Testing for Independence with BERET
Author(s): Duyeol Lee* and Kai Zhang and Michael Kosorok
Companies: University of North Carolina at Chapel Hill and University of North Carolina, Chapel Hill and University of North Carolina at Chapel Hill
Keywords: Independence test; Nonparametric inference; Binary Expansion; Multivariate

Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics which are complete sufficient statistics for dependence. In this paper, we develop a new test through an ensemble method utilizing both the sum of squared symmetry statistics and distance correlation. Simulation studies suggest that the proposed method improves the power while preserving the clear interpretation of the binary expansion testing. We further extend this method to tests of independence of random vectors in arbitrary dimension. The proposed binary expansion randomized ensemble test (BERET) transforms the multivariate independence testing problem into a univariate one through random projections. The power of the proposed method is illustrated with many simulated and real data examples.

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

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