Supplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes

Abstract

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.



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.1569726

Publisher
figshare

DOI
10.6084/m9.figshare.1569726

Relations
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

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