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pro vyhledávání: '"Wadsworth, Jennifer L."'
It is no secret that statistical modelling often involves making simplifying assumptions when attempting to study complex stochastic phenomena. Spatial modelling of extreme values is no exception, with one of the most common such assumptions being st
Externí odkaz:
http://arxiv.org/abs/2409.16373
In situations where both extreme and non-extreme data are of interest, modelling the whole data set accurately is important. In a univariate framework, modelling the bulk and tail of a distribution has been extensively studied before. However, when m
Externí odkaz:
http://arxiv.org/abs/2209.05795
Classical models for multivariate or spatial extremes are mainly based upon the asymptotically justified max-stable or generalized Pareto processes. These models are suitable when asymptotic dependence is present, i.e., the joint tail decays at the s
Externí odkaz:
http://arxiv.org/abs/2105.05314
Publikováno v:
Environmetrics, e2671 (2021)
Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, non-stationarity will often prevail. Current methods for modelling non-s
Externí odkaz:
http://arxiv.org/abs/2101.07167
Publikováno v:
Journal of Multivariate Analysis 2021, Volume 184, 104736
Vine copulas are a type of multivariate dependence model, composed of a collection of bivariate copulas that are combined according to a specific underlying graphical structure. Their flexibility and practicality in moderate and high dimensions have
Externí odkaz:
http://arxiv.org/abs/2012.09623
Autor:
Nolde, Natalia, Wadsworth, Jennifer L.
The study of multivariate extremes is dominated by multivariate regular variation, although it is well known that this approach does not provide adequate distinction between random vectors whose components are not always simultaneously large. Various
Externí odkaz:
http://arxiv.org/abs/2012.00990
Publikováno v:
Extremes 2023, Volume 26, Pages 669-713
The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial and spatio-temporal settings. After standardizing the marginal distributions and applying an appropriate li
Externí odkaz:
http://arxiv.org/abs/2011.04486
Autor:
Huser, Raphaël, Wadsworth, Jennifer L.
The classical modeling of spatial extremes relies on asymptotic models (i.e., max-stable processes or $r$-Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at finite levels, empirical evidence often suggests tha
Externí odkaz:
http://arxiv.org/abs/2007.00774
Publikováno v:
Spatial Statistics 2021, Volume 41, 100482
Recent extreme value theory literature has seen significant emphasis on the modelling of spatial extremes, with comparatively little consideration of spatio-temporal extensions. This neglects an important feature of extreme events: their evolution ov
Externí odkaz:
http://arxiv.org/abs/2002.04362
Autor:
Wadsworth, Jennifer L., Tawn, Jonathan
Currently available models for spatial extremes suffer either from inflexibility in the dependence structures that they can capture, lack of scalability to high dimensions, or in most cases, both of these. We present an approach to spatial extreme va
Externí odkaz:
http://arxiv.org/abs/1912.06560