DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Jihwan | ko |
dc.contributor.author | Zheng, Faye | ko |
dc.contributor.author | Doerge, R. W. | ko |
dc.contributor.author | Chun, Hyonho | ko |
dc.date.accessioned | 2019-08-20T01:20:02Z | - |
dc.date.available | 2019-08-20T01:20:02Z | - |
dc.date.created | 2019-08-20 | - |
dc.date.created | 2019-08-20 | - |
dc.date.created | 2019-08-20 | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | JOURNAL OF APPLIED STATISTICS, v.45, no.14, pp.2677 - 2696 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.uri | http://hdl.handle.net/10203/264303 | - |
dc.description.abstract | Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches. | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data | - |
dc.type | Article | - |
dc.identifier.wosid | 000443889300011 | - |
dc.identifier.scopusid | 2-s2.0-85042110575 | - |
dc.type.rims | ART | - |
dc.citation.volume | 45 | - |
dc.citation.issue | 14 | - |
dc.citation.beginningpage | 2677 | - |
dc.citation.endingpage | 2696 | - |
dc.citation.publicationname | JOURNAL OF APPLIED STATISTICS | - |
dc.identifier.doi | 10.1080/02664763.2018.1437123 | - |
dc.contributor.localauthor | Chun, Hyonho | - |
dc.contributor.nonIdAuthor | Oh, Jihwan | - |
dc.contributor.nonIdAuthor | Zheng, Faye | - |
dc.contributor.nonIdAuthor | Doerge, R. W. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Conditional independence | - |
dc.subject.keywordAuthor | graphical model | - |
dc.subject.keywordAuthor | Hilbert-Schmidt independence criterion | - |
dc.subject.keywordAuthor | partial correlation coefficient | - |
dc.subject.keywordAuthor | reproducing kernel Hilbert space | - |
dc.subject.keywordAuthor | support vector regression | - |
dc.subject.keywordPlus | LIKELIHOOD-ESTIMATION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | REGRESSION | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.