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

Threshold selection is a fundamental problem in any threshold-based extremevalue analysis. While models are asymptotically motivated, selecting anappropriate threshold for finite samples can be difficult through standardmethods. Inference can also be highly sensitive to the choice of threshold. Toolow a threshold choice leads to bias in the fit of the extreme value model,while too high a choice leads to unnecessary additional uncertainty in theestimation of model parameters. In this paper, we develop a novel methodologyfor automated threshold selection that directly tackles this bias-variancetrade-off. We also develop a method to account for the uncertainty in thisthreshold choice and propagate this uncertainty through to high quantileinference. Through a simulation study, we demonstrate the effectiveness of ourmethod for threshold selection and subsequent extreme quantile estimation. Weapply our method to the well-known, troublesome example of the River Nidddataset.

Automated threshold selection and associated inference uncertainty for univariate extremes