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|a (DE-627)JST052610942
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|a (JST)3647494
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|a DE-627
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|e rakwb
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|a eng
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|a Heffernan, Janet E.
|e verfasserin
|4 aut
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|a A Conditional Approach for Multivariate Extreme Values
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|c 2004
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|a Text
|b txt
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|a Computermedien
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|a Online-Ressource
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|a Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d-dimensional random variable. Existing approaches are based on limiting arguments in which all components of the variable become large at the same rate. This limit approach is inappropriate when the extreme values of all the variables are unlikely to occur together or when interest is in regions of the support of the joint distribution where only a subset of components is extreme. In practice this restricts existing methods to applications where d is typically 2 or 3. Under an assumption about the asymptotic form of the joint distribution of a d-dimensional random variable conditional on its having an extreme component, we develop an entirely new semiparametric approach which overcomes these existing restrictions and can be applied to problems of any dimension. We demonstrate the performance of our approach and its advantages over existing methods by using theoretical examples and simulation studies. The approach is used to analyse air pollution data and reveals complex extremal dependence behaviour that is consistent with scientific understanding of the process. We find that the dependence structure exhibits marked seasonality, with extremal dependence between some pollutants being significantly greater than the dependence at non-extreme levels.
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|a Copyright 2004 The Royal Statistical Society
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|a Air Pollution
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|a Asymptotic Independence
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|a Bootstrap
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|a Conditional Distribution
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|a Gaussian Estimation
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|a Multivariate Extreme Value Theory
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|a Semiparametric Modelling
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Statistical models
|x Parametric models
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
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|a Environmental studies
|x Environmental quality
|x Environmental degradation
|x Environmental pollution
|x Air pollution
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|a Mathematics
|x Mathematical procedures
|x Mathematical extrapolation
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|a Mathematics
|x Pure mathematics
|x Geometry
|x Geometric shapes
|x Curves
|x Asymptotes
|x Asymptotic value
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|a Mathematics
|x Pure mathematics
|x Probability theory
|x Random variables
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|a Environmental studies
|x Environmental quality
|x Environmental degradation
|x Environmental pollution
|x Pollutants
|x Air pollutants
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|a Environmental studies
|x Atmospheric sciences
|x Climatology
|x Seasons
|x Winter
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|a Behavioral sciences
|x Psychology
|x Cognitive psychology
|x Cognitive processes
|x Thought processes
|x Reasoning
|x Inference
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|a Environmental studies
|x Atmospheric sciences
|x Climatology
|x Seasons
|x Summer
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|a research-article
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|a Tawn, Jonathan A.
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of the Royal Statistical Society. Series B (Statistical Methodology)
|d Blackwell Publishers
|g 66(2004), 3, Seite 497-546
|w (DE-627)30219746X
|w (DE-600)1490719-7
|x 14679868
|7 nnns
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|g volume:66
|g year:2004
|g number:3
|g pages:497-546
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|u https://www.jstor.org/stable/3647494
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|d 66
|j 2004
|e 3
|h 497-546
|