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- W2953187029 abstract "Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or nonparanormal--for high dimensional inference. Just as additive models extend linear models by replacing linear functions with a set of one-dimensional smooth functions, the nonparanormal extends the normal by transforming the variables by smooth functions. We derive a method for estimating the nonparanormal, study the method's theoretical properties, and show that it works well in many examples." @default.
- W2953187029 created "2019-06-27" @default.
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- W2953187029 date "2009-03-03" @default.
- W2953187029 modified "2023-09-27" @default.
- W2953187029 title "The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs" @default.
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