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dc.contributor.authorDavison, Anthony C.
dc.contributor.authorHuser, Raphaël
dc.date.accessioned2015-08-03T12:33:50Z
dc.date.available2015-08-03T12:33:50Z
dc.date.issued2015-04-10
dc.identifier.issn23268298
dc.identifier.doi10.1146/annurev-statistics-010814-020133
dc.identifier.urihttp://hdl.handle.net/10754/564140
dc.description.abstractStatistics of extremes concerns inference for rare events. Often the events have never yet been observed, and their probabilities must therefore be estimated by extrapolation of tail models fitted to available data. Because data concerning the event of interest may be very limited, efficient methods of inference play an important role. This article reviews this domain, emphasizing current research topics. We first sketch the classical theory of extremes for maxima and threshold exceedances of stationary series. We then review multivariate theory, distinguishing asymptotic independence and dependence models, followed by a description of models for spatial and spatiotemporal extreme events. Finally, we discuss inference and describe two applications. Animations illustrate some of the main ideas. © 2015 by Annual Reviews. All rights reserved.
dc.publisherAnnual Reviews
dc.subjectAsymptotic dependence
dc.subjectAsymptotic independence
dc.subjectExtrapolation
dc.subjectGeneralized extreme value distribution
dc.subjectGeneralized Pareto distribution
dc.subjectMax-stability
dc.subjectPareto process
dc.subjectPeaks over thresholds
dc.subjectPoisson process
dc.titleStatistics of Extremes
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalAnnual Review of Statistics and Its Application
dc.contributor.institutionEcole Polytechnique Fédérale de LausanneLausanne, Switzerland
kaust.personHuser, Raphaël


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