IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p1114-d1371903.html
   My bibliography  Save this article

Tolerance Interval for the Mixture Normal Distribution Based on Generalized Extreme Value Theory

Author

Listed:
  • Junjun Jiao

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
    These authors contributed equally to this work.)

  • Ruijie Guan

    (School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China
    These authors contributed equally to this work.)

Abstract

For a common type of mixture distribution, namely the mixture normal distribution, existing methods for constructing its tolerance interval are unsatisfactory for cases of small sample size and large content. In this study, we propose a method to construct a tolerance interval for the mixture normal distribution based on the generalized extreme value theory. The proposed method is implemented on simulated as well as real-life datasets and its performance is compared with the existing methods.

Suggested Citation

  • Junjun Jiao & Ruijie Guan, 2024. "Tolerance Interval for the Mixture Normal Distribution Based on Generalized Extreme Value Theory," Mathematics, MDPI, vol. 12(7), pages 1-11, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1114-:d:1371903
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/1114/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/7/1114/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shin‐Fu Tsai, 2020. "Approximate two‐sided tolerance intervals for normal mixture distributions," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 367-382, September.
    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.

      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:gam:jmathe:v:12:y:2024:i:7:p:1114-:d:1371903. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.