The paper introduces a graphical conditional extreme value model with asymmetric Gaussian residuals that captures both asymptotic dependence and independence and supports stepwise inference in high dimensions.
The SCMEVMs do slightly underestimate the probabilities, however, we anticipate this could be resolved by increasing the size of the prediction datasets
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Conditional Extremes with Graphical Models
The paper introduces a graphical conditional extreme value model with asymmetric Gaussian residuals that captures both asymptotic dependence and independence and supports stepwise inference in high dimensions.