Extremal graphical modeling with latent variables

arXiv:2403.09604v1 Announce Type: cross
Abstract: Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events. Prior work on learning these graphs from data has focused on the setting where all relevant variables are observed. For the popular class of H\"usler-Reiss models, we propose the \texttt{eglatent} method, a tractable convex program for learning extremal graphical models in the presence of latent variables. Our approach decomposes the H\"usler-Reiss precision …

abstract arxiv class data encode events graphs math.st modeling multivariate popular prior risk stat.me stat.ml stat.th tool type variables work