Extremal properties of the multivariate extended skew-normal distribution

09/28/2018
by   Boris Beranger, et al.
0

The skew-normal and related families are flexible and asymmetric parametric models suitable for modelling a diverse range of systems. We show that the multivariate maximum of a high-dimensional extended skew-normal random sample has asymptotically independent components and derive the speed of convergence of the joint tail. To describe the possible dependence among the components of the multivariate maximum, we show that under appropriate conditions an approximate multivariate extreme-value distribution that leads to a rich dependence structure can be derived.

READ FULL TEXT
research
05/08/2018

Extremal properties of the extended skew-normal distribution

The skew-normal and related families are flexible and asymmetric paramet...
research
05/11/2020

Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator

We obtain explicit Wasserstein distance error bounds between the distrib...
research
09/27/2022

A new method to construct high-dimensional copulas with Bernoulli and Coxian-2 distributions

We propose an approach to construct a new family of generalized Farlie-G...
research
10/24/2017

On the Conditional Distribution of a Multivariate Normal given a Transformation - the Linear Case

We show that the orthogonal projection operator onto the range of the ad...
research
09/05/2018

Determining the Dependence Structure of Multivariate Extremes

In multivariate extreme value analysis, the nature of the extremal depen...
research
02/02/2019

Dependence properties and Bayesian inference for asymmetric multivariate copulas

We study a broad class of asymmetric copulas introduced by Liebscher (20...
research
12/17/2020

A geometric investigation into the tail dependence of vine copulas

Vine copulas are a type of multivariate dependence model, composed of a ...

Please sign up or login with your details

Forgot password? Click here to reset