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

Gaussian scale mixtures are constructed as Gaussian processes with a randomvariance. They have non-Gaussian marginals and can exhibit asymptoticdependence unlike Gaussian processes, which are asymptotically independentexcept in the case of perfect dependence. In this paper, we study in detail theextremal dependence properties of Gaussian scale mixtures and we unify andextend general results on their joint tail decay rates in both asymptoticdependence and independence cases. Motivated by the analysis of spatialextremes, we propose several flexible yet parsimonious parametric copula modelsthat smoothly interpolate from asymptotic dependence to independence andinclude the Gaussian dependence as a special case. We show how these new modelscan be fitted to high threshold exceedances using a censored likelihoodapproach, and we demonstrate that they provide valuable information about tailcharacteristics. Our parametric approach outperforms the widely usednonparametric $\chi$ and $\bar\chi$ statistics often used to guide model choiceat an exploratory stage by borrowing strength across locations for betterestimation of the asymptotic dependence class. We demonstrate the capacity ofour methodology by adequately capturing the extremal properties of wind speeddata collected in the Pacific Northwest, US.

Bridging Asymptotic Independence and Dependence in Spatial Extremes Using Gaussian Scale Mixtures