Understand
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs.
- In Gaussian models, tests of conditional independence are typically based on Pearson correlations, and high-dimensional consistency results have been obtained for the PC algorithm in this setting.
- We prove that high-dimensional consistency carries over to the broader class of Gaussian copula or \textit{nonparanormal} models when using rank-based measures of correlation.
- For graphs with bounded degree, our result is as strong as prior Gaussian results.
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Diego Colombo, Marloes H. Maathuis, Markus Kalisch, and Thomas S. Richardson, Learning high-dimensional directed acyclic graphs with latent and selection variables , Ann. Statist. (2012), no. 40, 294–321
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Closest in time.
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Closest in time.
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