English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multivariate extreme value distributions are a common choice for modellingmultivariate extremes. In high dimensions, however, the construction offlexible and parsimonious models is challenging. We propose to combinebivariate extreme value distributions into a Markov random field with respectto a tree. Although in general not an extreme value distribution itself, thisMarkov tree is attracted by a multivariate extreme value distribution. Thelatter serves as a tree-based approximation to an unknown extreme valuedistribution with the given bivariate distributions as margins. Given data, welearn an appropriate tree structure by Prim's algorithm with estimated pairwiseupper tail dependence coefficients or Kendall's tau values as edge weights. Thedistributions of pairs of connected variables can be fitted in various ways.The resulting tree-structured extreme value distribution allows for inferenceon rare event probabilities, as illustrated on river discharge data from theupper Danube basin.

Modelling multivariate extreme value distributions via Markov trees