Show simple item record

dc.contributor.authorStrokorb, Kirstin
dc.contributor.authorOesting, Marco
dc.contributor.authorDe Fondeville, Raphaël
dc.date.accessioned2025-02-06T10:04:15Z
dc.date.available2025-02-06T10:04:15Z
dc.date.issued2025-02
dc.identifier.urihttp://publications.mfo.de/handle/mfo/4206
dc.description.abstractThreshold exceedances of stochastic processes in space and time often appear to be more localized the more extreme they are. While classical regularly varying stochastic processes cannot model this effect, we introduce an adapted version of regular variation, where a suitable domain-scaling can be incorporated to accommodate this behaviour. Our theory is inspired by the triangular array convergence of domain-scaled maxima of Gaussian processes to a Brown-Resnick process and turns out to be natural in this context. We study key properties of the resulting tail process and demonstrate its ability to approximate conditional exceedance probabilities of Gaussian processes. Mathematical convenience arises from the recently rediscovered concept of vague convergence based on boundedness.en_US
dc.description.sponsorshipThe authors would like to thank the Mathematisches Forschungsinstitut Oberwolfach for the kind hospitality during our two weeks at the institute in March 2024 supported through the program "Oberwolfach Research Fellows", which has allowed us to kickstart this research. KS wishes to thank her home institution, Cardiff University, for granting her research leave in autumn 2024 to complete the research and writing of this manuscript, including financial support for visiting MO at the University of Stuttgart during this time.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesOberwolfach Preprints;2025-02
dc.titleDomain-Scaled Regular Variation: Mathematical Foundations for a New Tail Processen_US
dc.typePreprinten_US
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWP-2025-02
local.scientificprogramOWRF 2024en_US
local.series.idOWP-2025-02en_US
dc.identifier.ppn1917638620


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record