The nonparanormal SKEPTIC

Han Liu*, Fang Han, Ming Yuan, John Lafferty, Larry Wasserman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We propose a semiparametric method we call the nonparanormal SKEPTIC for estimating high dimensional undirected graphical models. The underlying model is the nonparanormal family proposed by Liu et al. (2009). The method exploits nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. In high dimensional settings, we prove that the nonparanormal SKEPTIC achieves the optimal parametric rate of convergence for both graph and parameter estimation. This result suggests that the nonparanormal graphical model can be a safe replacement for the Gaussian graphical model, even when the data are Gaussian.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1415-1422
Number of pages8
Volume2
StatePublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume2

Conference

Conference29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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