PuSH - Publikationsserver des Helmholtz Zentrums München

Lederer, J.* ; Müller, C.L.

Topology adaptive graph estimation in high dimensions.

Mathematics 10:1244 (2022)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Graphical Models ; High-dimensional Statistics ; Tuning Parameters
ISSN (print) / ISBN 2227-7390
e-ISSN 2227-7390
Zeitschrift Mathematics
Quellenangaben Band: 10, Heft: 8, Seiten: , Artikelnummer: 1244 Supplement: ,
Verlag MDPI
Verlagsort Basel, Switzerland
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Open Access Publication Funds of the Ruhr-Universitat Bochum