TY - JOUR
T1 - Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution
AU - Krupskii, Pavel
AU - Joe, Harry
AU - Lee, David
AU - Genton, Marc G.
N1 - 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.
PY - 2017/11/2
Y1 - 2017/11/2
N2 - The multivariate Hüsler–Reiß 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 Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.
AB - The multivariate Hüsler–Reiß 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 Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.
UR - http://hdl.handle.net/10754/626114
UR - http://www.sciencedirect.com/science/article/pii/S0047259X17301525
UR - http://www.scopus.com/inward/record.url?scp=85034260290&partnerID=8YFLogxK
U2 - 10.1016/j.jmva.2017.10.006
DO - 10.1016/j.jmva.2017.10.006
M3 - Article
VL - 163
SP - 80
EP - 95
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
SN - 0047-259X
ER -