ArticleAdvances in neural information processing systems2021
A Kernel-based Test of Independence for Cluster-correlated Data.
Article in Advances in neural information processing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multivariate differential association analysis.Stat (International Statistical Institute) · 2024Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- A fast kernel independence test for cluster-correlated data.Scientific reports · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful kernel-based statistic for assessing the generalized dependence between two multivariate variables. However, independence testing based on the HSIC is not directly possible for cluster-correlated data. Such a correlation pattern among the observations arises in many practical situations, e.g., family-based and longitudinal data, and requires proper accommodation. Therefore, we propose a novel HSIC-based independence test to evaluate the dependence between two multivariate variables based on cluster-correlated data. Using the previously proposed empirical HSIC as our test statistic, we derive its asymptotic distribution under the null hypothesis of independence between the two variables but in the presence of sample correlation. Based on both simulation studies and real data analysis, we show that, with clustered data, our approach effectively controls type I error and has a higher statistical power than competing methods.
Identifiers
36590676PMC9801702What OpenQuestion holds
Registered trials
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