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Full likelihood inference for max‐stable data
Author(s) -
Huser Raphaël,
Dombry Clément,
Ribatet Mathieu,
Genton Marc G.
Publication year - 2019
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.218
Subject(s) - inference , computer science , multivariate statistics , statistical inference , computation , expectation–maximization algorithm , maximization , logistic regression , maximum likelihood , algorithm , machine learning , artificial intelligence , mathematical optimization , mathematics , statistics
We show how to perform full likelihood inference for max‐stable multivariate distributions or processes based on a stochastic expectation–maximization algorithm, which combines statistical and computational efficiency in high dimensions. The good performance of this methodology is demonstrated by simulation based on the popular logistic and Brown–Resnick models, and it is shown to provide computational time improvements with respect to a direct computation of the likelihood. Strategies to further reduce the computational burden are also discussed.