11 papers
Principal Component Analysis for Multivariate Extremes
Dan Cooley, Anne Sabourin, Troy Wixson
This chapter explores ways to reduce the dimensionality of the data while preserving key information relevant to the analysis of multivariate extreme values.
Polar Depth for Potentially Heavy-Tailed Data
Stephan Clemençon, Carlos Fernándes, Pavlo Mozharovskyi +1
Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. T…
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
Baptiste Leroux, Clément Dombry, Anne Sabourin
We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effective…
Zero-couplings of infinite measures with cyclically monotone support and multivariate regular variation
Alexandre Reber, Anne Sabourin, Johan Segers +1
We study cyclically monotone transport plans between measures in , the class of Borel measures on that are finite on sets…
Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast
Nathan Huet, Philippe Naveau, Anne Sabourin
Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew surges along the French Atlantic coas…
Weak Signals and Heavy Tails: Learning Theory meets Extreme Value Analysis
Stephan Clémençon, Anne Sabourin
The masses of data now available have opened up the prospect of discovering weak signals using machine-learning algorithms, with a view to predictive or interpretation tasks. As th…