Supplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes
Type
DatasetKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2016Permanent link to this record
http://hdl.handle.net/10754/624775
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<p>In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of points is a very challenging problem 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.</p>Citation
Castruccio, S., Huser, R., & Genton, M. G. (2016). High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes. Figshare. https://doi.org/10.6084/m9.figshare.1569726Publisher
figshareRelations
Is Supplement To:- [Article]
High-order Composite Likelihood Inference for Max-Stable Distributions and Processes 2015:1 Journal of Computational and Graphical Statistics. DOI: 10.1080/10618600.2015.1086656 HANDLE: 10754/583973
ae974a485f413a2113503eed53cd6c53
10.6084/m9.figshare.1569726