@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",
}