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Titel:

PC Algorithm for Nonparanormal Graphical Models

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Gaussian copula, graphical model, model selection, multivariate normal distribution, nonparanormal distribution
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Journal of Machine Learning Research
Jahr:
2013
Band / Volume:
14
Jahr / Monat:
2013-01
Quartal:
1. Quartal
Monat:
Jan
Heft / Issue:
1
Seitenangaben Beitrag:
3365-3383
Sprache:
en
Volltext / DOI:
doi:10.5555/2567709.2567770
WWW:
Journal of Machine Learning Research
Verlag / Institution:
JMLR. org
Publikationsdatum:
01.01.2013
Format:
Text
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