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• W2953003747 abstract "In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is among the most challenging problems 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." @default.
• W2953003747 created "2019-06-27" @default.
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• W2953003747 creator A5048011656 @default.
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• W2953003747 date "2014-11-01" @default.
• W2953003747 modified "2023-09-27" @default.
• W2953003747 title "High-order Composite Likelihood Inference for Max-Stable Distributions and Processes" @default.
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• W2953003747 hasPublicationYear "2014" @default.
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