Abstract
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.
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Huser, R., Davison, A.C. & Genton, M.G. Likelihood estimators for multivariate extremes. Extremes 19, 79–103 (2016). https://doi.org/10.1007/s10687-015-0230-4
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DOI: https://doi.org/10.1007/s10687-015-0230-4
Keywords
- Asymptotic relative efficiency
- Censored likelihood
- Logistic model
- Multivariate extremes
- Pairwise likelihood
- Point process approach