Home > Research > Publications & Outputs > Sub-asymptotic results to motivate a new condit...

Electronic data

  • LugrinTawnDavison

    Rights statement: 12m

    Accepted author manuscript, 217 KB, PDF document

    Embargo ends: 1/01/50

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License


Text available via DOI:

View graph of relations

Sub-asymptotic results to motivate a new conditional multivariate extremes model.

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>27/06/2021
Publication StatusAccepted/In press
<mark>Original language</mark>English


Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then the approximation may describe observed extremes poorly, and bias can only be reduced by choosing a very high threshold, at the cost of unacceptably large variance in any subsequent tail inference. An alternative is to use sub-asymptotic extremal models, which introduce more parameters but can provide better fits for lower thresholds. We consider this problem in the context of the Heffernan--Tawn conditional tail model for multivariate extremes, which has found wide use due to its flexible handling of dependence in high-dimensional applications. Recent extensions of this model appear to improve joint tail inference. We seek a sub-asymptotic justification for why these extensions work, and show that they can improve convergence rates by an order of magnitude for certain copulas. We also propose a class of extensions of them that may have wider value for statistical inference in multivariate extremes.