000180416 001__ 180416
000180416 005__ 20190316235445.0
000180416 0247_ $$2doi$$a10.1214/11-STS376
000180416 022__ $$a0883-4237
000180416 02470 $$2ISI$$a000306194500001
000180416 037__ $$aARTICLE
000180416 245__ $$aStatistical Modeling of Spatial Extremes
000180416 260__ $$bInstitute of Mathematical Statistics$$c2012
000180416 269__ $$a2012
000180416 336__ $$aJournal Articles
000180416 520__ $$aThe areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rainfall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for successful spatial modeling of extremes.
000180416 6531_ $$aAnnual maximum analysis
000180416 6531_ $$aBayesian hierarchical model
000180416 6531_ $$aBrown-Resnick process
000180416 6531_ $$acomposite likelihood
000180416 6531_ $$acopula
000180416 6531_ $$aenvironmental data analysis
000180416 6531_ $$aGaussian process
000180416 6531_ $$ageneralized extreme-value distribution
000180416 6531_ $$ageostatistics
000180416 6531_ $$alatent variable
000180416 6531_ $$amax-stable process
000180416 6531_ $$astatistics of extremes
000180416 6531_ $$aMax-Stable Processes
000180416 6531_ $$aSample Extremes
000180416 6531_ $$aLikelihood Inference
000180416 6531_ $$aWind Speeds
000180416 6531_ $$aMultivariate
000180416 6531_ $$aGeostatistics
000180416 6531_ $$aDependence
000180416 6531_ $$aValues
000180416 6531_ $$aCopulas
000180416 6531_ $$aTrend
000180416 700__ $$0240476$$g111184$$aDavison, A. C.
000180416 700__ $$aPadoan, S. A.
000180416 700__ $$aRibatet, M.$$0243109$$g182332
000180416 773__ $$j27$$tStatistical Science$$q161-186
000180416 8564_ $$uhttps://infoscience.epfl.ch/record/180416/files/2012-73.pdf$$zn/a$$s2078967$$yn/a
000180416 8564_ $$uhttps://infoscience.epfl.ch/record/180416/files/2012-74.pdf$$zn/a$$s846560
000180416 909C0 $$xU10124$$0252136$$pSTAT
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000180416 917Z8 $$x111184
000180416 937__ $$aEPFL-ARTICLE-180416
000180416 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000180416 980__ $$aARTICLE