The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

Simple item page
dc.creatorLiu, Han
dc.creatorLafferty, John
dc.creatorWasserman, Larry
dc.date2009-03-03
dc.date.accessioned2026-07-07T12:48:57Z
dc.date.available2026-07-07T12:48:57Z
dc.descriptionRecent 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.
dc.identifierhttps://arxiv.org/abs/0903.0649
dc.identifierhttp://arxiv.org/abs/0903.0649
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222237
dc.subjectMachine Learning
dc.titleThe Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
dc.typetext

Files

Collections

arXiv