The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
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
Text
Liu et al. "The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs." Journal of Machine Learning Research, 2009.Markdown
[Liu et al. "The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/liu2009jmlr-nonparanormal/)BibTeX
@article{liu2009jmlr-nonparanormal,
title = {{The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs}},
author = {Liu, Han and Lafferty, John and Wasserman, Larry},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {2295-2328},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/liu2009jmlr-nonparanormal/}
}