Skip to content
DraconiticResearch
Search Sources API
185,139 studies31 sourcesUpdated 15 min ago
Peer-reviewedLicence not statedThe Annals of Statistics

Learning extremal graphical structures in high dimensions

  1. 1Research Center for Statistics, University of Geneva
  2. 2Département de mathématiques, Université du Québec à Montréal
  3. 3Department of Statistical Sciences, University of Toronto

Related

  1. Interior Curvature Estimates for the Graphical Scalar Curvature Equation in All Dimensions
    Guohuan Qiu, Jin Yan
    arXivPreprintarXiv licenceFull textAnalysis & PDEsGeometry & topologyarXiv
  2. Clustering extremal dependence structures via Wasserstein distances between angular measures
    Kamal Gasser, Francesco Ragone, Johan Segers
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Pointwise Structures and Background Perturbation Analyses for Thermal Non-equilibrium Irrotational Flows in Three Space Dimensions
    Shijin Deng, Xiaochun Yang
    arXivPreprintarXiv licenceFull textAnalysis & PDEsarXiv
  4. High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
    Percy S. Zhai, Ping-Shou Zhong, Wei Biao Wu
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  5. Comment on Vogels et al.'s (2024) "Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison": An updated performance check using BGGM
    Joris Mulder
    arXivPreprintCC BY-SA 4.0Full textStatisticsarXiv
BibTeX
@article{engelke2026learning,
  title = {{Learning extremal graphical structures in high dimensions}},
  author = {Sebastian Engelke and Michaël Lalancette and Stanislav Volgushev A},
  journal = {The Annals of Statistics},
  year = {2026},
  month = jun,
  doi = {10.1214/25-aos2594},
  url = {http://arxiv.org/abs/2111.00840},
}
RIS
TY  - JOUR
TI  - Learning extremal graphical structures in high dimensions
AU  - Sebastian Engelke
AU  - Michaël Lalancette
AU  - Stanislav Volgushev A
PY  - 2026
DA  - 2026/06/18
JO  - The Annals of Statistics
DO  - 10.1214/25-aos2594
UR  - http://arxiv.org/abs/2111.00840
SN  - 0090-5364
SN  - 2168-8966
ER  -