Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data

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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.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2018
Language
English
Article Type
Article
Citation

JOURNAL OF APPLIED STATISTICS, v.45, no.14, pp.2677 - 2696

ISSN
0266-4763
DOI
10.1080/02664763.2018.1437123
URI
http://hdl.handle.net/10203/264303
Appears in Collection
MA-Journal Papers(저널논문)
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