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dc.contributor.authorVettori, Sabrina
dc.contributor.authorHuser, Raphaël
dc.contributor.authorSegers, Johan
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2019-07-25T07:25:02Z
dc.date.available2019-07-25T07:25:02Z
dc.date.issued2019-08-29
dc.identifier.citationVettori, S., Huser, R., Segers, J., & Genton, M. G. (2019). Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes. Journal of Computational and Graphical Statistics, 29(1), 174–190. doi:10.1080/10618600.2019.1647847
dc.identifier.doi10.1080/10618600.2019.1647847
dc.identifier.urihttp://hdl.handle.net/10754/656177
dc.description.abstractDescribing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue, we develop a novel statistical method that describes extremal dependence taking advantage of the inherent tree-based dependence structure of the max-stable nested logistic distribution, and which identifies possible clusters of extreme variables using reversible jump Markov chain Monte Carlo techniques. Parsimonious representations are achieved when clusters of extreme variables are found to be completely independent. Moreover, we significantly decrease the computational complexity of full likelihood inference by deriving a recursive formula for the likelihood function of the nested logistic model. The method’s performance is verified through extensive simulation experiments which also compare different likelihood procedures. The new methodology is used to investigate the dependence relationships between extreme concentrations of multiple pollutants in California and how these concentrations are related to extreme weather conditions. Overall, we show that our approach allows for the representation of complex extremal dependence structures and has valid applications in multivariate data analysis, such as air pollution monitoring, where it can guide policymaking. Supplementary materials for this article are available online.
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/10618600.2019.1647847
dc.rightsArchived with thanks to Journal of Computational and Graphical Statistics
dc.subjectAir pollution
dc.subjectExtreme event
dc.subjectFast likelihood inference
dc.subjectNested logistic model
dc.subjectReversible jump Markov chain Monte Carlo
dc.titleBayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes
dc.typeArticle
dc.contributor.departmentEntrepreneurship Center
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Computational and Graphical Statistics
dc.rights.embargodate2020-01-01
dc.eprint.versionPost-print
dc.contributor.institutionUniversité catholique de Louvain, Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA), Louvain-la-Neuve, Belgium
pubs.publication-statusAccepted
kaust.personVettori, Sabrina
kaust.personHuser, Raphaël
kaust.personGenton, Marc G.
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.9172628
refterms.dateFOA2019-07-25T07:25:03Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Dataset]</i> <br/> Vettori, S., Huser, R., Segers, J., &amp; Genton, M. G. (2019). <i>Bayesian model averaging over tree-based dependence structures for multivariate extremes</i> [Data set]. Taylor &amp; Francis. https://doi.org/10.6084/M9.FIGSHARE.9172628. DOI: <a href="https://doi.org/10.6084/m9.figshare.9172628" >10.6084/m9.figshare.9172628</a> Handle: <a href="http://hdl.handle.net/10754/664785" >10754/664785</a></a></li></ul>
dc.date.published-online2019-08-29
dc.date.published-print2020-01-02


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