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Activity Number: 185
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319110 View Presentation
Title: Testing Mutual Independence in High Dimension via Distance Covariance
Author(s): SHUN YAO* and Xianyang Zhang and Xiaofeng Shao
Companies: University of Illinois at Urbana-Champaign and Texas A&M University and University of Illinois at Urbana-Champaign
Keywords: Banded dependence ; Degenerate U-statistic ; Distance correlation ; High dimensionality ; Hoeffding decomposition

In this paper, we introduce a L2 type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed based on the pairwise distance covariance and it accounts for the non-linear and non-monotone dependences among the data, which cannot be fully captured by the existing tests based on either Pearson correlation or rank correlation. Our test can be conveniently implemented in practice as the limiting null distribution of the test statistic is shown to be standard normal. It exhibits excellent finite sample performance in our simulation studies even when sample size is small albeit dimension is high.

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

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