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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Both marginal and dependence features must be described when modelling theextremes of a stationary time series. There are standard approaches to marginalmodelling, but long- and short-range dependence of extremes may both appear. Inapplications, an assumption of long-range independence often seems reasonable,but short-range dependence, i.e., the clustering of extremes, needs attention.The extremal index $0<\theta\le 1$ is a natural limiting measure of clustering,but for wide classes of dependent processes, including all stationary Gaussianprocesses, it cannot distinguish dependent processes from independent processeswith $\theta=1$. Eastoe and Tawn (2012) exploit methods from multivariateextremes to treat the subasymptotic extremal dependence structure of stationarytime series, covering both $0<\theta<1$ and $\theta=1$, through theintroduction of a threshold-based extremal index. Inference for theirdependence models uses an inefficient stepwise procedure that has variousweaknesses and has no reliable assessment of uncertainty. We overcome theseissues using a Bayesian semiparametric approach. Simulations and the analysisof a UK daily river flow time series show that the new approach providesimproved efficiency for estimating properties of functionals of clusters.

Bayesian Uncertainty Management in Temporal Dependence of Extremes