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Topology adaptive graph estimation in high dimensions.
Mathematics 10:1244 (2022)
We introduce Graphical TREX (GTREX), a novel method for graph estimation in highdimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compared GTREX with standard methods on a new simulation setup that was designed to assess accurately the strengths and shortcomings of different methods. These simulations showed that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperformed other standard methods over a large spectrum of scenarios. Moreover, we show that GTREX can rival this scheme and, therefore, can provide competitive graph estimation without the need for tuning parameter calibration.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Graphical Models ; High-dimensional Statistics ; Tuning Parameters
ISSN (print) / ISBN
2227-7390
e-ISSN
2227-7390
Journal
Mathematics
Quellenangaben
Volume: 10,
Issue: 8,
Article Number: 1244
Publisher
MDPI
Publishing Place
Basel, Switzerland
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Open Access Publication Funds of the Ruhr-Universitat Bochum