High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

Stefano Castruccio, Raphaël Huser, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.
Original languageEnglish (US)
Pages (from-to)1212-1229
Number of pages18
JournalJournal of Computational and Graphical Statistics
Issue number4
StatePublished - Nov 10 2016

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KAUST Repository Item: Exported on 2020-10-01


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