IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i3d10.1007_s00180-024-01540-y.html
   My bibliography  Save this article

A precise and efficient exceedance-set algorithm for detecting environmental extremes

Author

Listed:
  • Thomas Suesse

    (University of Wollongong
    Department of Geography
    Martin-Luther University Halle-Wittenberg)

  • Alexander Brenning

    (Department of Geography
    Michael Stifel Center Jena for Data-Driven and Simulation Science)

Abstract

Inference for predicted exceedance sets is important for various environmental issues such as detecting environmental anomalies and emergencies with high confidence. A critical part is to construct inner and outer predicted exceedance sets using an algorithm that samples from the predictive distribution. The simple currently used sampling procedure can lead to misleading conclusions for some locations due to relatively large standard errors when proportions are estimated from independent observations. Instead we propose an algorithm that calculates probabilities numerically using the Genz–Bretz algorithm, which is based on quasi-random numbers leading to more accurate inner and outer sets, as illustrated on rainfall data in the state of Paraná, Brazil.

Suggested Citation

  • Thomas Suesse & Alexander Brenning, 2025. "A precise and efficient exceedance-set algorithm for detecting environmental extremes," Computational Statistics, Springer, vol. 40(3), pages 1583-1595, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01540-y
    DOI: 10.1007/s00180-024-01540-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01540-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01540-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01540-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.