The Journal of Machine Learning Research
Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
The Journal of Machine Learning Research
The huge package for high-dimensional undirected graph estimation in R
The Journal of Machine Learning Research
Multiple Response Regression for Gaussian Mixture Models with Known Labels
Statistical Analysis and Data Mining
Collective inference for network data with copula latent markov networks
Proceedings of the sixth ACM international conference on Web search and data mining
NP-MuScL: unsupervised global prediction of interaction networks from multiple data sources
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
CODA: high dimensional copula discriminant analysis
The Journal of Machine Learning Research
Robust methods for inferring sparse network structures
Computational Statistics & Data Analysis
PC algorithm for nonparanormal graphical models
The Journal of Machine Learning Research
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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.