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dc.contributor.authorCamilo, Daniela Castro
dc.contributor.authorHuser, Raphaël
dc.date.accessioned2019-07-24T11:07:10Z
dc.date.available2017-12-28T07:32:14Z
dc.date.available2019-07-24T11:07:10Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10754/626512
dc.description.abstractTo disentangle the complex non-stationary dependence structure of precipitation extremes over the entire contiguous U.S., we propose a flexible local approach based on factor copula models. Our sub-asymptotic spatial modeling framework yields non-trivial tail dependence structures, with a weakening dependence strength as events become more extreme; a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate the local extremal behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity, which allows us to gain in flexibility. By adopting a local censored likelihood approach, we make inference on a fine spatial grid, and we perform local estimation by taking advantage of distributed computing resources and the embarrassingly parallel nature of this estimation procedure. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. We carry out an extensive simulation study to show that our approach can adequately capture complex, non-stationary dependencies, in addition, our study of U.S. winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events.
dc.description.sponsorshipWe thank Luigi Lombardo (University of Twente) for his cartographic support and Eduardo Gonz´alez (KAUST) for his computational support. We extend our thanks to Dan Cooley (Colorado State University) for helpful comments and suggestions. Support from the KAUST Supercomputing Laboratory and access to Shaheen is also gratefully acknowledged. We are particularly grateful to the two referees for their comments and suggestions that have led to a much improved version of this paper. This publication is based upon work supported by KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.
dc.description.sponsorshipArchived with thanks to Journal of the American Statistical Association
dc.publisherTaylor and Francis
dc.relation.urlhttp://arxiv.org/abs/1710.00875v1
dc.relation.urlhttp://arxiv.org/pdf/1710.00875v1
dc.subjectfactor copula model
dc.subjectlocal likelihood
dc.subjectnon-stationarity
dc.subjectspatial extremes
dc.subjectthreshold exceedance
dc.titleLocal likelihood estimation of complex tail dependence structures in high dimensions, applied to US precipitation extremes
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalAccepted by Journal of the American Statistical Association
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK.
dc.identifier.arxivid1710.00875
kaust.personCamilo, Daniela Castro
kaust.personHuser, Raphaël
refterms.dateFOA2018-06-13T12:45:38Z


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