Robustification of the PC-Algorithm for Directed Acyclic Graphs

The PC-algorithm was shown to be a powerful method for estimating the equivalence class of a potentially very high-dimensional acyclic directed graph (DAG) with the corresponding Gaussian distribution. Here we propose a computationally eficient robustification of the PC-algorithm and prove its consi...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2008-12, Vol.17 (4), p.773-789
Main Authors: Kalisch, Markus, Bühlmann, Peter
Format: Article
Language:English
Subjects:
Algorithms
Comparative analysis
Consistent estimators
Covariance matrices
Directed acyclic graphs
Equivalence relation
Estimating techniques
Gaussian distributions
Gaussian Graphical Methods
Gaussian graphical modeling
Graphs
Heuristics
Machine learning
Normal distribution
Outliers
Partial correlation analysis
Qn-estimator
R package pcalg
Random variables
Skeleton
Studies
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Summary:The PC-algorithm was shown to be a powerful method for estimating the equivalence class of a potentially very high-dimensional acyclic directed graph (DAG) with the corresponding Gaussian distribution. Here we propose a computationally eficient robustification of the PC-algorithm and prove its consistency. Furthermore, we compare the robustified and standard version of the PC-algorithm on simulated data using the new corresponding R package pcalg.
ISSN:1061-8600
1537-2715
DOI:10.1198/106186008X381927