@article{3427302163164ce5a542548c64493dc0,
title = "Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the H{\"u}sler–Rei{\ss} distribution",
abstract = "The multivariate H{\"u}sler–Rei{\ss} copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of H{\"u}sler–Rei{\ss} parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the H{\"u}sler–Rei{\ss} copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the H{\"u}sler–Rei{\ss} copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.",
author = "Pavel Krupskii and Harry Joe and David Lee and Genton, {Marc G.}",
note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST), Discovery Grant No. 8698 from the Natural Sciences and Engineering Research Council of Canada, a Collaborative Research Team grant for the project Copula Dependence Modeling: Theory and Applications of the Canadian Statistical Sciences Institute (CANSSI), and a University of British Columbia four-year doctoral fellowship. We are grateful to the two referees, the Associate Editor, and the Editor-in-Chief for their comments.",
year = "2017",
month = nov,
day = "2",
doi = "10.1016/j.jmva.2017.10.006",
language = "English (US)",
volume = "163",
pages = "80--95",
journal = "Journal of Multivariate Analysis",
issn = "0047-259X",
publisher = "Academic Press Inc.",
}