IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6281781.html
   My bibliography  Save this article

Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy

Author

Listed:
  • Fan Yang
  • Hu Ren
  • Zhili Hu

Abstract

The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

Suggested Citation

  • Fan Yang & Hu Ren & Zhili Hu, 2019. "Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, May.
    • Handle: RePEc:hin:jnlmpe:6281781
    DOI: 10.1155/2019/6281781
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/6281781.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2019/6281781.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/6281781?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
    ---><---

    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:hin:jnlmpe:6281781. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.