TY - JOUR
T1 - Likelihood estimators for multivariate extremes
AU - Huser, Raphaël
AU - Davison, Anthony C.
AU - Genton, Marc G.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2015/11/17
Y1 - 2015/11/17
N2 - The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
AB - The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
UR - http://hdl.handle.net/10754/583987
UR - http://link.springer.com/10.1007/s10687-015-0230-4
UR - http://www.scopus.com/inward/record.url?scp=84956633601&partnerID=8YFLogxK
U2 - 10.1007/s10687-015-0230-4
DO - 10.1007/s10687-015-0230-4
M3 - Article
SN - 1386-1999
VL - 19
SP - 79
EP - 103
JO - Extremes
JF - Extremes
IS - 1
ER -