Rejoinder to "Statistical Modeling of Spatial Extremes"

@article{Davison2012RejoinderT,
  title={Rejoinder to "Statistical Modeling of Spatial Extremes"},
  author={Anthony C. Davison and Simone A. Padoan and Mathieu Ribatet},
  journal={arXiv: Methodology},
  year={2012}
}
Rejoinder to "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378]. 
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On generalized max-linear models in max-stable random fields
TLDR
A method to reconstruct a max-stable random field in C([0, 1] k ) by interpolating its realizations at finitely many points by using a generalized max-linear model.

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We discuss the analysis of the extremes of data by modelling the sizes and occurrence of exceedances over high thresholds. The natural distribution for such exceedances, the generalized Pareto…
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Max‐stable processes are the natural analogues of the generalized extreme value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are…
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Multivariate extreme value distributions arise as the limiting distributions of normalised componentwise maxima. They are often used to model multivariate data that can be regarded as the…
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Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More…
Discussions and Rejoinder on “Threshold modelling of spatially dependent non‐stationary extremes with application to hurricane‐induced wave heights”
Reference EPFL-ARTICLE-170694doi:10.1002/env.1125Afficher la publication dans Web of Science Notice creee le 2011-12-16, modifiee le 2016-08-09
[Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Rejoinder
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Statistics of Extremes
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    International Encyclopedia of Statistical Science
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TLDR
This article reviews multivariate theory, distinguishing asymptotic independence and dependence models, followed by a description of models for spatial and spatiotemporal extreme events, and discusses inference and describe two applications.
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