The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

@article{Liu2009TheNS,
  title={The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs},
  author={Han Liu and John D. Lafferty and Larry A. Wasserman},
  journal={Journal of Machine Learning Research},
  year={2009},
  volume={10},
  pages={2295-2328}
}
Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We sho w w to use a semiparametric Gaussian copula—or “nonparanormal”—for high dimensional infere nc . 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 varia bles by smooth functions. We derive a method for estimating… CONTINUE READING
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