IDEAS home Printed from https://ideas.repec.org/a/spr/sankha/v84y2022i2d10.1007_s13171-020-00203-6.html
   My bibliography  Save this article

Unit Nadarajah-Haghighi Generated Family of Distributions: Properties and Applications

Author

Listed:
  • Suleman Nasiru

    (University for Development Studies)

  • Abdul Ghaniyyu Abubakari

    (University for Development Studies)

  • John Abonongo

    (University for Development Studies)

Abstract

The unit Nadarajah-Haghighi (UNH) class of distributions was developed and its statistical properties investigated in this study. The generator was used to develop the UNH Weibull and UNH log-logistic distributions. For some given parameter values it was realized that the density and failure rate functions of the UNH Weibull and UNH log-logistic distributions can exhibit different kinds of shapes making the distributions suitable for modeling dataset that exhibit some of these shapes. Monte Carlo simulations were performed to examine how the maximum likelihood estimators and ordinary least squares estimators perform with regard to estimating the parameters of the distributions and the results indicated that the maximum likelihood performs better than the ordinary least squares. Applications of the UNH Weibull distribution revealed that it can provide good parametric fit to given datasets.

Suggested Citation

  • Suleman Nasiru & Abdul Ghaniyyu Abubakari & John Abonongo, 2022. "Unit Nadarajah-Haghighi Generated Family of Distributions: Properties and Applications," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 450-476, August.
    • Handle: RePEc:spr:sankha:v:84:y:2022:i:2:d:10.1007_s13171-020-00203-6
    DOI: 10.1007/s13171-020-00203-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13171-020-00203-6
    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/s13171-020-00203-6?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.

    References listed on IDEAS

    as
    1. Suleman Nasiru, 2018. "Extended Odd Fréchet-G Family of Distributions," Journal of Probability and Statistics, Hindawi, vol. 2018, pages 1-12, December.
    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. Lea Anzagra & Solomon Sarpong & Suleman Nasiru, 2022. "Odd Chen-G Family of Distributions," Annals of Data Science, Springer, vol. 9(2), pages 369-391, April.

    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:sankha:v:84:y:2022:i:2:d:10.1007_s13171-020-00203-6. 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: 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.