Publications associées à Tail Risk Inference via Expectiles in Heavy-Tailed Time Series

Publications associées (15)

Lattice Models and Conformal Field Theory

Clément Hongler, Franck Raymond Gabriel, Francesco Spadaro

This book introduces the mathematical ideas connecting Statistical Mechanics and Conformal Field Theory (CFT). Building advanced structures on top of more elementary ones, the authors map out a well-posed road from simple lattice models to CFTs. Structure ...

Courant Institute of Mathematical Sciences2024

Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential

Jean-Louis Scartezzini, Alina Ursula Rosa Walch, Federico Amato

With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1 ...

SPRINGER2022

Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning

Devis Tuia, Benjamin Alexander Kellenberger

The goal of habitat suitability mapping is to predict the lo-cations in which a given species could be present. This is typically accomplished by statistical models which use envi-ronmental variables to predict species observation data. The relationship be ...

IEEE2022

Optimal lower bounds on hitting probabilities for non-linear systems of stochastic fractional heat equations

Robert Dalang, Fei Pu

We consider a system of d non-linear stochastic fractional heat equations in spatial dimension 1 driven by multiplicative d-dimensional space-time white noise. We establish a sharp Gaussian-type upper bound on the two-point probability density function of ...

ELSEVIER2021

Group testing for connected communities

Christina Fragouli, Suhas Diggavi, Pavlos Nikolopoulos

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depe ...

MICROTOME PUBLISHING2021

Tighter thermodynamic bound on the speed limit in systems with unidirectional transitions

Daniel Maria Busiello

We consider a general discrete state-space system with both unidirectional and bidirectional links. In contrast to bidirectional links, there is no reverse transition along the unidirectional links Herein, we first compute the statistical length and the th ...

2020

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

Emmanuel Abbé

We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and b ...

Microtome Publishing2020

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

Lenka Zdeborová

p>We study the dynamics of optimization and the generalization properties of one-hidden layer neural networks with quadratic activation function in the overparametrized regime where the layer width m is larger than the input dimension d. We conside ...

Curran Associates, Inc.2020

The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Nicolas Macris, Jean François Emmanuel Barbier

In recent years important progress has been achieved towards proving the validity of the replica predictions for the (asymptotic) mutual information (or free energy) in Bayesian inference problems. The proof techniques that have emerged appear to be quite ...

SPRINGER HEIDELBERG2019

Global Solutions To Stochastic Reaction-Diffusion Equations With Super-Linear Drift And Multiplicative Noise

Robert Dalang

Let xi(t, x) denote space-time white noise and consider a reaction-diffusion equation of the form ...

INST MATHEMATICAL STATISTICS2019

Scaling Limits of Solutions of Linear Stochastic Differential Equations Driven by Levy White Noises

Michaël Unser, Julien René Fageot

Consider a random process s that is a solution of the stochastic differential equation Ls = w with L a homogeneous operator and w a multidimensional Levy white noise. In this paper, we study the asymptotic effect of zooming in or zooming out of the process ...

2019

A nonparametric method for producing isolines of bivariate exceedance probabilities

Emeric Rolland Georges Thibaud

We present a method for drawing isolines indicating regions of equal joint exceedance probability for bivariate data. The method relies on bivariate regular variation, a dependence framework widely used for extremes. The method we utilize for characterizin ...

SPRINGER2019

Sphere-of-Influence Graphs in Normed Spaces

János Pach, Márton Naszódi

We showthat any k-th closed sphere-of-influence graph in a d-dimensional normed space has a vertex of degree less than 5dk, thus obtaining a common generalization of results of Furedi and Loeb (Proc AmMath Soc 121(4): 1063-1073, 1994 [1]) and Guibas et al. ...

SPRINGER2018

Mechanical reliability and durability of SOFC stacks. Part I: Modelling of the effect of operating conditions and design alternatives on the reliability

Jan Van Herle, Daniel Favrat, Arata Nakajo

Electrochemical and mechanical aspects in solid oxide fuel cell stack must be understood to meet the reliability targets for market implementation. This study presents a stack modelling framework that combines thermo-electrochemical models, including degra ...

Elsevier2012

From error probability to information theoretic signal and image processing

Torsten Butz

The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...

EPFL2003