Semiparametric bivariate modelling with flexible extremal dependence

Leonelli, Manuele; Gamerman, Dani

Publication:
Semiparametric bivariate modelling with flexible extremal dependence

dc.contributor.author	Leonelli, Manuele
dc.contributor.author	Gamerman, Dani
dc.contributor.ror	https://ror.org/02jjdwm75
dc.date.accessioned	2025-03-18T17:51:33Z
dc.date.available	2025-03-18T17:51:33Z
dc.date.issued	2019-05-28
dc.description.abstract	Inference over multivariate tails often requires a number of assumptions which may affect the assessment of the extreme dependence structure. Models are usually constructed in such a way that extreme components can either be asymptotically dependent or be independent of each other. Recently, there has been an increasing interest on modelling multivariate extremes more flexibly, by allowing models to bridge both asymptotic dependence regimes. Here we propose a novel semiparametric approach which allows for a variety of dependence patterns, be them extremal or not, by using in a model-based fashion the full dataset.We build on previous work for inference on marginal exceedances over a high, unknown threshold, by combining it with flexible, semiparametric copula specifications to investigate extreme dependence, thus separately modelling marginals and dependence structure. Because of the generality of our approach, bivariate problems are investigated here due to computational challenges, but multivariate extensions are readily available. Empirical results suggest that our approach can provide sound uncertainty statements about the possibility of asymptotic independence, and we propose a criterion to quantify the presence of either extreme regime which performs well in our applications when compared to others available. Estimation of functions of interest for extremes is performed via MCMC algorithms. Attention is also devoted to the prediction of new extreme observations. Our approach is evaluated through simulations, applied to real data and assessed against competing approaches. Evidence demonstrates that the bulk of the data do not bias and improve the inferential process for extremal dependence in our applications.
dc.description.peerreviewed	yes
dc.description.status	Published
dc.format	application/pdf
dc.identifier.citation	Leonelli, M., & Gamerman, D. (2020). Semiparametric bivariate modelling with flexible extremal dependence. Statistics and Computing, 30(2), 221-236. https://doi.org/10.1007/s11222-019-09878-w.
dc.identifier.doi	https://doi.org/10.1007/s11222-019-09878-w
dc.identifier.issn	1573-1375
dc.identifier.uri	https://hdl.handle.net/20.500.14417/3666
dc.journal.title	Statistics and Computing
dc.language.iso	en
dc.page.final	236
dc.page.initial	221
dc.page.total	16
dc.publisher	Springer Nature Link
dc.relation.department	Computer Science and AI
dc.relation.entity	IE University
dc.relation.school	IE School of Science & Technology
dc.rights	Attribution 4.0 International
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/deed
dc.subject.keyword	Asymptotic dependence
dc.subject.keyword	Copulae
dc.subject.keyword	GPD distribution
dc.subject.keyword	High quantiles
dc.subject.keyword	Prediction
dc.subject.keyword	Threshold estimation
dc.title	Semiparametric bivariate modelling with flexible extremal dependence
dc.type	info:eu-repo/semantics/article
dc.version.type	info:eu-repo/semantics/publishedVersion
dc.volume.number	30
dspace.entity.type	Publication
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