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• W2593417708 abstract "Max-stable processes provide canonical models for the dependence of multivariate extremes. Inference with such models has been challenging due to the lack of tractable likelihoods which has motivated use of composite likelihood methods Padoan et al. (J. Amer. Stat. Assoc. 105(489):263–277, 2010). In contrast, the finite dimensional cumulative distribution functions (CDFs) appear natural to work with, and are readily available or can be approximated well. Motivated by this fact, in this work we develop an M-estimation framework for max-stable models based on the continuous ranked probability score (CRPS) of multivariate CDFs. We start by establishing conditions for the consistency and asymptotic normality of the CRPS-based estimators in a general context. We then implement them in the max-stable setting and provide readily computable expressions for their asymptotic covariance matrices. The resulting point and asymptotic confidence interval estimates are illustrated over popular simulated models. They enjoy accurate coverages and offer an alternative to composite likelihood based methods." @default.
• W2593417708 created "2017-03-16" @default.
• W2593417708 creator A5038908067 @default.
• W2593417708 creator A5068784665 @default.
• W2593417708 date "2014-05-14" @default.
• W2593417708 modified "2023-09-28" @default.
• W2593417708 title "CRPS M-estimation for max-stable models" @default.
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• W2593417708 doi "https://doi.org/10.1007/s10687-014-0185-x" @default.
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