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• W2963716198 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." @default.
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• W2963716198 date "2012-06-26" @default.
• W2963716198 modified "2023-10-14" @default.
• W2963716198 title "The Nonparanormal SKEPTIC" @default.
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