The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Machine Learning
2009-03-05 v1
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.
Cite
@article{arxiv.0903.0649,
title = {The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs},
author = {Han Liu and John Lafferty and Larry Wasserman},
journal= {arXiv preprint arXiv:0903.0649},
year = {2009}
}