IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v78y2024i2p264-280.html
   My bibliography  Save this article

A gamma tail statistic and its asymptotics

Author

Listed:
  • Toshiya Iwashita
  • Bernhard Klar

Abstract

Asmussen and Lehtomaa [Distinguishing log‐concavity from heavy tails. Risks 5(10), 2017] introduced an interesting function g$$ g $$ which is able to distinguish between log‐convex and log‐concave tail behavior of distributions, and proposed a randomized estimator for g$$ g $$. In this paper, we show that g$$ g $$ can also be seen as a tool to detect gamma distributions or distributions with gamma tail. We construct a more efficient estimator ĝn$$ {\hat{g}}_n $$ based on U$$ U $$‐statistics, propose several estimators of the (asymptotic) variance of ĝn$$ {\hat{g}}_n $$, and study their performance by simulations. Finally, the methods are applied to several datasets of daily precipitation.

Suggested Citation

  • Toshiya Iwashita & Bernhard Klar, 2024. "A gamma tail statistic and its asymptotics," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 264-280, May.
    • Handle: RePEc:bla:stanee:v:78:y:2024:i:2:p:264-280
    DOI: 10.1111/stan.12316
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/stan.12316
    Download Restriction: no
    File URL: https://libkey.io/10.1111/stan.12316?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
    ---><---

    References listed on IDEAS

    as
    1. Lehtomaa, Jaakko, 2015. "Limiting behaviour of constrained sums of two variables and the principle of a single big jump," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 157-163.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
    2. Bernhard Klar, 2025. "A Pareto Tail Plot Without Moment Restrictions," The American Statistician, Taylor & Francis Journals, vol. 79(2), pages 156-166, April.
    3. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.

    More about this item

    Statistics

    Access and download statistics

    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:bla:stanee:v:78:y:2024:i:2:p:264-280. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.