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Title:

PC Algorithm for Nonparanormal Graphical Models

Document type:
Zeitschriftenaufsatz
Author(s):
Harris, Naftali; Drton, Mathias
Abstract:
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian models, tests of conditional independence are typically based on Pearson correlations, and high-dimensional consistency results have been obtained for the PC algorithm in this setting. Analyzing the error propagation from marginal to partial correlations, we prove that high-dimensional consistency carries over to a broader class of Gaussian copula or nonparanor...     »
Keywords:
Gaussian copula, graphical model, model selection, multivariate normal distribution, nonparanormal distribution
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Machine Learning Research
Year:
2013
Journal volume:
14
Year / month:
2013-01
Quarter:
1. Quartal
Month:
Jan
Journal issue:
1
Pages contribution:
3365-3383
Language:
en
Fulltext / DOI:
doi:10.5555/2567709.2567770
WWW:
Journal of Machine Learning Research
Publisher:
JMLR. org
Date of publication:
01.01.2013
Format:
Text
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