Robust estimation of the conditional stable tail dependence function

Yuri Goegebeur; Armelle Guillou; Jing Qin

doi:10.1007/s10463-022-00839-1

Robust estimation of the conditional stable tail dependence function

Yuri Goegebeur, Armelle Guillou^*, Jing Qin

^*Kontaktforfatter

University of Strasbourg

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Abstract

We propose a robust estimator of the stable tail dependence function in the case where random covariates are recorded. Under suitable assumptions, we derive the finite-dimensional weak convergence of the estimator properly normalized. The performance of our estimator in terms of efficiency and robustness is illustrated through a simulation study. Our methodology is applied on a real dataset of sale prices of residential properties.

Originalsprog	Engelsk
Tidsskrift	Annals of the Institute of Statistical Mathematics
Vol/bind	75
Udgave nummer	2
Sider (fra-til)	201-231
ISSN	0020-3157
DOI	https://doi.org/10.1007/s10463-022-00839-1
Status	Udgivet - apr. 2023

Bibliografisk note

Funding Information:
The authors sincerely thank the editor, associate editor and the referees for their helpful comments and suggestions that led to substantial improvement of the paper. The research of Armelle Guillou was supported by the French National Research Agency under the grant ANR-19-CE40-0013-01/ExtremReg project and an International Emerging Action (IEA-00179). Computation/simulation for the work described in this paper was supported by the DeIC National HPC Centre, SDU.

Publisher Copyright:
© 2022, The Institute of Statistical Mathematics, Tokyo.

Dokumenter og links

10.1007/s10463-022-00839-1

https://hal.archives-ouvertes.fr/hal-03132477v2/file/RobustCondStableTail-Halv3.pdf

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

@article{71bbc60b20534e76aa12e24e47096221,

title = "Robust estimation of the conditional stable tail dependence function",

abstract = "We propose a robust estimator of the stable tail dependence function in the case where random covariates are recorded. Under suitable assumptions, we derive the finite-dimensional weak convergence of the estimator properly normalized. The performance of our estimator in terms of efficiency and robustness is illustrated through a simulation study. Our methodology is applied on a real dataset of sale prices of residential properties.",

keywords = "Empirical processes, Local estimation, Multivariate extreme value statistics, Robustness, Stable tail dependence function",

author = "Yuri Goegebeur and Armelle Guillou and Jing Qin",

note = "Funding Information: The authors sincerely thank the editor, associate editor and the referees for their helpful comments and suggestions that led to substantial improvement of the paper. The research of Armelle Guillou was supported by the French National Research Agency under the grant ANR-19-CE40-0013-01/ExtremReg project and an International Emerging Action (IEA-00179). Computation/simulation for the work described in this paper was supported by the DeIC National HPC Centre, SDU. Publisher Copyright: {\textcopyright} 2022, The Institute of Statistical Mathematics, Tokyo.",

year = "2023",

month = apr,

doi = "10.1007/s10463-022-00839-1",

language = "English",

volume = "75",

pages = "201--231",

journal = "Annals of the Institute of Statistical Mathematics",

issn = "0020-3157",

publisher = "Springer",

number = "2",

}

TY - JOUR

T1 - Robust estimation of the conditional stable tail dependence function

AU - Goegebeur, Yuri

AU - Guillou, Armelle

AU - Qin, Jing

N1 - Funding Information: The authors sincerely thank the editor, associate editor and the referees for their helpful comments and suggestions that led to substantial improvement of the paper. The research of Armelle Guillou was supported by the French National Research Agency under the grant ANR-19-CE40-0013-01/ExtremReg project and an International Emerging Action (IEA-00179). Computation/simulation for the work described in this paper was supported by the DeIC National HPC Centre, SDU. Publisher Copyright: © 2022, The Institute of Statistical Mathematics, Tokyo.

PY - 2023/4

Y1 - 2023/4

N2 - We propose a robust estimator of the stable tail dependence function in the case where random covariates are recorded. Under suitable assumptions, we derive the finite-dimensional weak convergence of the estimator properly normalized. The performance of our estimator in terms of efficiency and robustness is illustrated through a simulation study. Our methodology is applied on a real dataset of sale prices of residential properties.

AB - We propose a robust estimator of the stable tail dependence function in the case where random covariates are recorded. Under suitable assumptions, we derive the finite-dimensional weak convergence of the estimator properly normalized. The performance of our estimator in terms of efficiency and robustness is illustrated through a simulation study. Our methodology is applied on a real dataset of sale prices of residential properties.

KW - Empirical processes

KW - Local estimation

KW - Multivariate extreme value statistics

KW - Robustness

KW - Stable tail dependence function

U2 - 10.1007/s10463-022-00839-1

DO - 10.1007/s10463-022-00839-1

M3 - Journal article

AN - SCOPUS:85133013716

SN - 0020-3157

VL - 75

SP - 201

EP - 231

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

IS - 2

ER -

Robust estimation of the conditional stable tail dependence function

Abstract

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater