IDEAS home Printed from https://ideas.repec.org/a/arp/ajoams/2021p106-112.html
   My bibliography  Save this article

Bayesian Methods and Maximum Likelihood Estimations of Exponential Censored Time Distribution with Cure Fraction

Author

Listed:
  • Dr. Al Omari Mohammed Ahmed

    (Department of Mathematics, Faculty of Arts and Science in Qilwah, AlBaha University, Baha, Saudi Arabia)

Abstract

This paper is focused on estimating the parameter of Exponential distribution under right-censored data with cure fraction. The maximum likelihood estimation and Bayesian approach were used. The Bayesian method is implemented using gamma, Jeffreys, and extension of Jeffreys priors with two loss functions, which are; squared error loss function and Linear Exponential Loss Function (LINEX). The methods of the Bayesian approach are compared to maximum likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) to determine the best for estimating the parameter of Exponential distribution under right-censored data with cure fraction. The results show that the Bayesian with gamma prior under LINEX loss function is a better estimation of the parameter of Exponential distribution with cure fraction based on right-censored data.

Suggested Citation

  • Dr. Al Omari Mohammed Ahmed, 2021. "Bayesian Methods and Maximum Likelihood Estimations of Exponential Censored Time Distribution with Cure Fraction," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(2), pages 106-112, 04-2021.
    • Handle: RePEc:arp:ajoams:2021:p:106-112
    DOI: 10.32861/ajams.72.106.112
    as

    Download full text from publisher

    File URL: https://www.arpgweb.com/pdf-files/ajams7(2)106-112.pdf
    Download Restriction: no
    File URL: https://www.arpgweb.com/journal/17/archive/04-2021/2/7
    Download Restriction: no
    File URL: https://libkey.io/10.32861/ajams.72.106.112?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
    ---><---

    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:arp:ajoams:2021:p:106-112. 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: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/index.php?ic=journal&journal=17&info=aims .

    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.