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Modeling multivariate extremes.

  • Published In: WIREs: Computational Statistics, 2024, v. 16, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Nolan, John P. 3 of 3

Abstract

The Central Limit Theorem justifies using a normal distribution when looking at sums of many terms. In a parallel way, extreme value distributions arise in the study of maxima of many terms. The goal of this paper is to briefly review the univariate theory of extremes based on the Fisher-Tippet-Gnedenko Theorem. We then state the basics of the multivariate theory, which is significantly more complicated because it requires a measure to define the distribution. Some properties of these laws are explored, including a description of the support, an expression for the density when it exists, and some examples that illustrate possible joint dependence structures. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:WIREs: Computational Statistics. 2024/03, Vol. 16, Issue 2, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1939-5108
  • DOI:10.1002/wics.1652
  • Accession Number:177737880
  • Copyright Statement:Copyright of WIREs: Computational Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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