The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

被引：0

作者：

Liu, Han ^{[1
]}

Lafferty, John ^{[1
]}

Wasserman, Larry ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2009年 / 10卷

关键词：

graphical models; Gaussian copula; high dimensional inference; sparsity; l(1) regularization; graphical lasso; paranormal; occult; MODEL; SELECTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2295 / 2328

页数：34

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