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Bibliographic Details
Main Author: Wan, Phyllis
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2411.00573
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author Wan, Phyllis
author_facet Wan, Phyllis
contents In this paper, we characterize the extremal dependence of $d$ asymptotically dependent variables by a class of random vectors on the $(d-1)$-dimensional hyperplane perpendicular to the diagonal vector $\mathbf1=(1,\ldots,1)$. This translates analyses of multivariate extremes to that on a linear vector space, opening up possibilities for applying existing statistical techniques that are based on linear operations. As an example, we demonstrate obtaining lower-dimensional approximations of the tail dependence through principal component analysis. Additionally, we show that the widely used Hüsler-Reiss family is characterized by a Gaussian family residing on the hyperplane.
format Preprint
id arxiv_https___arxiv_org_abs_2411_00573
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Characterizing extremal dependence on a hyperplane
Wan, Phyllis
Statistics Theory
62G32, 62H05
In this paper, we characterize the extremal dependence of $d$ asymptotically dependent variables by a class of random vectors on the $(d-1)$-dimensional hyperplane perpendicular to the diagonal vector $\mathbf1=(1,\ldots,1)$. This translates analyses of multivariate extremes to that on a linear vector space, opening up possibilities for applying existing statistical techniques that are based on linear operations. As an example, we demonstrate obtaining lower-dimensional approximations of the tail dependence through principal component analysis. Additionally, we show that the widely used Hüsler-Reiss family is characterized by a Gaussian family residing on the hyperplane.
title Characterizing extremal dependence on a hyperplane
topic Statistics Theory
62G32, 62H05
url https://arxiv.org/abs/2411.00573