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Document type:
Zeitschriftenaufsatz 
Author(s):
Harris, Naftali; Drton, Mathias 
Title:
PC Algorithm for Nonparanormal Graphical Models 
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:
Pages contribution:
3365-3383 
Language:
en 
Fulltext / DOI:
Publisher:
JMLR. org 
Date of publication:
01.01.2013 
Format:
Text