DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Hoseung | ko |
dc.contributor.author | Liu, Hongjiao | ko |
dc.contributor.author | Wu, Michael C. | ko |
dc.date.accessioned | 2024-04-25T11:00:18Z | - |
dc.date.available | 2024-04-25T11:00:18Z | - |
dc.date.created | 2024-04-25 | - |
dc.date.created | 2024-04-25 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v.12, no.1 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/10203/319233 | - |
dc.description.abstract | Cluster-correlated data receives a lot of attention in biomedical and longitudinal studies 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 combines kernel information so that the dependence structure in each cluster is fully considered and exhibits good performance under high dimensions. Moreover, a rapid p value approximation makes the new test fast applicable to large datasets. Numerical studies show that the new approach performs well in both synthetic and real world data. | - |
dc.language | English | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | A fast kernel independence test for cluster-correlated data | - |
dc.type | Article | - |
dc.identifier.wosid | 001015461100013 | - |
dc.identifier.scopusid | 2-s2.0-85144095297 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | SCIENTIFIC REPORTS | - |
dc.identifier.doi | 10.1038/s41598-022-26278-9 | - |
dc.contributor.localauthor | Song, Hoseung | - |
dc.contributor.nonIdAuthor | Liu, Hongjiao | - |
dc.contributor.nonIdAuthor | Wu, Michael C. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | DEPENDENCE | - |
dc.subject.keywordPlus | UNIFRAC | - |
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