Topology adaptive graph estimation in high dimensions
- We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional 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.
| Author: | Johannes LedererGND, Christian L. MüllerORCiDGND |
|---|---|
| URN: | urn:nbn:de:hbz:294-91975 |
| DOI: | https://doi.org/10.3390/math10081244 |
| Parent Title (English): | Mathematics |
| Publisher: | MDPI |
| Place of publication: | Basel |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2022/08/04 |
| Date of first Publication: | 2022/04/10 |
| Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
| Tag: | Open Access Fonds graphical models; high-dimensional statistics; tuning parameters |
| Volume: | 10 |
| Issue: | 8, Article 1244 |
| First Page: | 1244-1 |
| Last Page: | 1244-10 |
| Note: | Article Processing Charge funded by the Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publication Fund of Ruhr-Universität Bochum. |
| Dewey Decimal Classification: | Naturwissenschaften und Mathematik / Mathematik |
| open_access (DINI-Set): | open_access |
| faculties: | Fakultät für Mathematik |
| Licence (English): |  Creative Commons - CC BY 4.0 - Attribution 4.0 International |
