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Activity Number: 438 - Contributed Poster Presentations: Korean International Statistical Society
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Korean International Statistical Society
Abstract #323871
Title: A Fast Kernel Independence Test for Cluster-Correlated Data
Author(s): Hoseung Song* and Hongjiao Liu and Michael C. Wu
Companies: Fred Hutchinson Cancer Research Center and University of Washington and Fred Hutchinson Cancer Research Center
Keywords:
Abstract:

Cluster-correlated data receives a lot of attention in biomedical and longitudinal stuides and it is of interest to assess the generalized dependence between two multivariate variables under the cluster-correlated structure. The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful kernel-based test statistic that captures various dependence between two random vectors and can be applied to an arbitrary non-Euclidean domain. However, the existing HSIC is not directly applicable to cluster-correlated data. Therefore, we propose a HSIC-based test of independence for cluster-correlated data. The new test statistic can be applied to the unbalanced cluster design and exhibits good performance under high dimensions. Moreover, rapid $p$-value approximation makes the new test fast for large datasets. Numerical studies show that the new approach performs well in both synthetic and real world data.


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

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