Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (Q6482960)
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scientific article
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm |
scientific article |
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Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (English)
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March 2007
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asymptotic consistency
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DAG
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graphical model
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skeleton
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PC-Algorithm
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We consider the PC-algorithm for estimating the skeleton of a very high-dimensional acyclic directed graph (DAG) with corresponding Gaussian distribution. The PC-algorithm is computationally feasible for sparse problems with many nodes, i.e. variables, and it has the attractive property to automatically achieve high computational efficiency as a function of sparseness of the true underlying DAG. We prove consistency of the algorithm for very high-dimensional, sparse DAGs where the number of nodes is allowed to quickly grow with sample size n, as fast as O(n^a) for any 0.
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