Open AccessPosted Content
Sparse Structures for Multivariate Extremes
TL;DR: The different forms of extremal dependence that can arise between the largest observations of a multivariate random vector are described and identification of groups of variables which can be concomitantly extreme is addressed.
read more
Abstract: Extreme value statistics provides accurate estimates for the small occurrence probabilities of rare events. While theory and statistical tools for univariate extremes are well-developed, methods for high-dimensional and complex data sets are still scarce. Appropriate notions of sparsity and connections to other fields such as machine learning, graphical models and high-dimensional statistics have only recently been established. This article reviews the new domain of research concerned with the detection and modeling of sparse patterns in rare events. We first describe the different forms of extremal dependence that can arise between the largest observations of a multivariate random vector. We then discuss the current research topics including clustering, principal component analysis and graphical modeling for extremes. Identification of groups of variables which can be concomitantly extreme is also addressed. The methods are illustrated with an application to flood risk assessment.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The record-breaking compound hot and dry 2018 growing season in Germany
TL;DR: In this article, the authors investigate spring to autumn temperature and precipitation in Germany over the historical period and show that since measurements started in 1881, Germany has never experienced as hot and dry conditions during March to November as in 2018.
176
Guidelines for Studying Diverse Types of Compound Weather and Climate Events
Emanuele Bevacqua,Emanuele Bevacqua,Carlo De Michele,Colin Manning,Anaïs Couasnon,Andreia F. S. Ribeiro,Andreia F. S. Ribeiro,Alexandre M. Ramos,Edoardo Vignotto,Ana Bastos,Suzana Blesic,Fabrizio Durante,John K. Hillier,Sérgio C. Oliveira,Joaquim G. Pinto,Elisa Ragno,Pauline Rivoire,Kate Saunders,Karin van der Wiel,Wenyan Wu,Tianyi Zhang,Jakob Zscheischler,Jakob Zscheischler,Jakob Zscheischler +23 more
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.
170
Evaluating the dependence structure of compound precipitation and wind speed extremes
Jakob Zscheischler,Jakob Zscheischler,Philippe Naveau,Olivia Martius,Olivia Martius,Sebastian Engelke,Christoph C. Raible,Christoph C. Raible +7 more
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.
Introduction—applications
Warren N. Waggenspack
- 01 Jan 1996
TL;DR: An office suite groups together programs that are generally used in offices such as Microsoft Office, which allows one to work with words enter, edit, format text and incorporate graphics.
82
Advancing research on compound weather and climate events via large ensemble model simulations
Emanuele Bevacqua,Laura Suarez-Gutierrez,Aglaé Jézéquel,Flavio Lehner,Mathieu Vrac,Pascal Yiou,Jakob Zscheischler +6 more
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.
References
•Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
410.8K
•Proceedings Article
Fast algorithms for mining association rules
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 01 Jul 1998
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
•Book
An Introduction to Multivariate Statistical Analysis
T. W. Anderson
- 14 Sep 1984
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
9.7K
•Book
An Introduction to Statistical Modeling of Extreme Values
Stuart Coles
- 20 Aug 2001
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.