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Activity Number: 313 - Recent Developments in Statistical Inference Using Distance Correlation and Related Dependence Metrics
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #300000 Presentation
Title: Expected Conditional Characteristic Function-Based Measures for Testing Independence
Author(s): Xiangrong Yin* and Chenlu Ke
Companies: University of Kentucky and University of Kentucky
Keywords: Independence; characteristic function; kernel; ANOVA

We propose a novel class of independence measures for testing independence between two random vectors based on the discrepancy between the conditional and the marginal characteristic functions. If one of the variables is categorical, our asymmetric index can be redeemed as the between group dispersion in a kernel ANOVA decomposition and leads to more powerful tests than those relying on symmetric measures. We further established a large class of reproducing kernel Hilbert space, and discovered our measure is a subclass of the RKHS. In addition, our index is also applicable when both variables are continuous. We develop two empirical estimates and obtain their respective asymptotic distributions. We illustrate the advantages of our approach by numerical studies across a variety of settings.

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

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