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

Handle URI:
http://hdl.handle.net/10754/624775
Title:
Supplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes
Authors:
Castruccio, Stefano; Huser, Raphaël ( 0000-0002-1228-2071 ) ; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
<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>
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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
Issue Date:
2016
DOI:
10.6084/m9.figshare.1569726
Type:
Dataset
Is Supplement To:
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:http://hdl.handle.net/10754/583973
Appears in Collections:
Datasets; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCastruccio, Stefanoen
dc.contributor.authorHuser, Raphaëlen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-06-06T08:33:43Z-
dc.date.available2017-06-06T08:33:43Z-
dc.date.created2015-10-08en
dc.date.issued2016en
dc.identifier.citationCastruccio, 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.1569726en
dc.identifier.doi10.6084/m9.figshare.1569726en
dc.identifier.urihttp://hdl.handle.net/10754/624775-
dc.description.abstract<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>en
dc.format.extent100904 Bytesen
dc.publisherFigshareen
dc.rightsCC-BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMedicineen
dc.subjectNeuroscienceen
dc.subjectSociologyen
dc.titleSupplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processesen
dc.typeDataseten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
kaust.authorHuser, Raphaëlen
kaust.authorGenton, Marc G.en
dc.type.resourcePaperen
dc.relation.isSupplementToHigh-order Composite Likelihood Inference for Max-Stable Distributions and Processes 2015:1 Journal of Computational and Graphical Statisticsen
dc.relation.isSupplementToDOI:10.1080/10618600.2015.1086656en
dc.relation.isSupplementToHANDLE:http://hdl.handle.net/10754/583973en
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