Abstract
The cover image is based on the Advance Review Advances in statistical modeling of spatial extremes by Raphaël Huser and Jennifer L. Wadsworth., https://doi.org/10.1002/wics.1537.image
Highlights
KEYWORDS asymptotic dependence and independence, extreme-value theory, max-stable process, Pareto process, random scale mixture
While inverted max-stable (IMS), max-mixture, and random scale or location mixture models discussed in Sections 3.2 and 3.3 are designed to be fitted to peaks over high thresholds, we conclude this section by briefly presenting recent models designed for block maxima, which extend the class of max-stable processes to capture asymptotic independence
In contrast to Morris et al (2017) who proposed a skew-t process combined with a random partitioning mechanism to break down long-range dependence, the conditional spatial extremes model allows for very flexible forms of extremal dependence and can naturally capture both asymptotic dependence and independence
Summary
KEYWORDS asymptotic dependence and independence, extreme-value theory, max-stable process, Pareto process, random scale mixture A related limitation of max-stable and Pareto processes is that they are always asymptotically dependent, unless they are fully independent.
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