Modeling spatial processes with unknown extremal dependence class

Handle URI:
http://hdl.handle.net/10754/626518
Title:
Modeling spatial processes with unknown extremal dependence class
Authors:
Huser, Raphaël G.; Wadsworth, Jennifer L.
Abstract:
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
Issue Date:
17-Mar-2017
ARXIV:
arXiv:1703.06031
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1703.06031v2; http://arxiv.org/pdf/1703.06031v2
Appears in Collections:
Other/General Submission; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHuser, Raphaël G.en
dc.contributor.authorWadsworth, Jennifer L.en
dc.date.accessioned2017-12-28T07:32:14Z-
dc.date.available2017-12-28T07:32:14Z-
dc.date.issued2017-03-17en
dc.identifier.urihttp://hdl.handle.net/10754/626518-
dc.description.abstractMany environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1703.06031v2en
dc.relation.urlhttp://arxiv.org/pdf/1703.06031v2en
dc.rightsArchived with thanks to arXiven
dc.titleModeling spatial processes with unknown extremal dependence classen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionDepartment of Mathematics and Statistics, Lancaster Universityen
dc.identifier.arxividarXiv:1703.06031en
kaust.authorHuser, Raphaël G.en
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