# Extreme Dependence Models

@article{Branger2015ExtremeDM, title={Extreme Dependence Models}, author={B. B{\'e}ranger and Simone A. Padoan}, journal={arXiv: Methodology}, year={2015}, pages={325-352} }

Extreme values of real phenomena are events that occur with low frequency, but can have a large impact on real life. These are, in many practical problems, high-dimensional by nature (e.g. Tawn, 1990; Coles and Tawn, 1991). To study these events is of fundamental importance. For this purpose, probabilistic models and statistical methods are in high demand. There are several approaches to modelling multivariate extremes as described in Falk et al. (2011), linked to some extent. We describe an…

## 10 Citations

A semi‐parametric stochastic generator for bivariate extreme events

- Mathematics
- 2017

The analysis of multiple extreme values aims to describe the stochastic behaviour of observations in the joint upper tail of a distribution function. For instance, being able to simulate multivariate…

High-dimensional inference using the extremal skew-t process

- Mathematics
- 2019

Max-stable processes are a popular tool for the study of environmental extremes, and the extremal skew- t process is a general model that allows for a flexible extremal dependence structure. For…

Exploratory data analysis for moderate extreme values using non-parametric kernel methods

- Mathematics
- 2016

In many settings it is critical to accurately model the extreme tail behaviour of a random process. Non-parametric density estimation methods are commonly implemented as exploratory data analysis…

Estimation and uncertainty quantification for extreme quantile regions

- Mathematics, Computer Science
- 2019

Univariate and bivariate schemes for estimating extreme quantile regions under the Bayesian paradigm that outperforms existing approaches and provides natural measures of quantile region estimate uncertainty are developed.

Bayesian Inference for the Extremal Dependence

- Mathematics
- 2016

A simple approach for modeling multivariate extremes is to consider the vector of component-wise maxima and their max-stable distributions. The extremal dependence can be inferred by estimating the…

Joint inference on extreme expectiles for multivariate heavy-tailed distributions

- Mathematics
- 2020

The notion of expectiles, originally introduced in the context of testing for homoscedasticity and conditional symmetry of the error distribution in linear regression, induces a law-invariant,…

Strong Consistency of Nonparametric Bayesian Inferential Methods for Multivariate Max-Stable Distributions

- Mathematics
- 2019

Predicting extreme events is important in many applications in risk analysis. The extreme-value theory suggests modelling extremes by max-stable distributions. The Bayesian approach provides a…

Tail density estimation for exploratory data analysis using kernel methods

- MathematicsJournal of Nonparametric Statistics
- 2018

ABSTRACT It is often critical to accurately model the upper tail behaviour of a random process. Nonparametric density estimation methods are commonly implemented as exploratory data analysis…

Models for extremal dependence derived from skew-symmetric families

- Mathematics
- 2015

Skew-symmetric families of distributions such as the skew-normal and skew-$t$ represent supersets of the normal and $t$ distributions, and they exhibit richer classes of extremal behaviour. By…

Consistency of Bayesian Inference for Multivariate Max-Stable Distributions.

- Mathematics
- 2019

Predicting extreme events is important in many applications in risk analysis. The extreme-value theory suggests modelling extremes by max-stable distributions. The Bayesian approach provides a…

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