# Structure learning for extremal tree models

@article{Engelke2020StructureLF, title={Structure learning for extremal tree models}, author={Sebastian Engelke and Stanislav Volgushev}, journal={arXiv: Methodology}, year={2020} }

Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we develop a data-driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and a new summary statistic, which we call the extremal variogram, can be used as weights for a…

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