Sparse Structures for Multivariate Extremes

TL;DR: In this paper, the authors consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified.

TL;DR: In this article, the authors introduce a new metric that measures whether the tails of bivariate distributions show a similar dependence structure across different datasets and evaluate the dependence structure of wind and precipitation extremes.

TL;DR: In this article , the authors focus on four event types arising from different combinations of climate variables across space and time, and demonstrate that robust analyses of compound events -such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events - require data with very large sample size.

TL;DR: This work defines the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables and proposes a computationally highly efficient algorithm that estimates the causal structure.

TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and modeling extreme value values in sequences.

TL;DR: Kurosh and Levitzki as discussed by the authors, on the radical of a general ring and three problems concerning nil rings, Bull Amer Math Soc vol 49 (1943) pp 913-919 10 -, On the structure of algebraic algebras and related rings.

TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.