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