IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i7p2139-2153.html
   My bibliography  Save this article

Applications of an extended geometric Brownian motion degradation model

Author

Listed:
  • Yu-Sheng Hsu
  • Pei-Chun Chen
  • Ming-Yung Lee
  • Cheng-Hsun Wu

Abstract

In order to modify the restriction that the path of the geometric Brownian motion can never reach zero, we consider an extended degradation model based on geometric Brownian motion. This model incorporates some important stochastic processes, such as geometric Brownian motion and the sinh-Gaussian process. We determine the convergence behavior of the first passage time as the random effect vanishes. The parameters of the model are estimated using the maximum likelihood method based on the first passage time observations. Both the explicit forms and the asymptotic properties of the estimators are provided. Our model can be applied to measure the brightness of LED lamps and is a reasonable extension of the original brightness model to situations in which the brightness reaches zero. The performance of the proposed model is discussed based on real data analysis and simulations.

Suggested Citation

  • Yu-Sheng Hsu & Pei-Chun Chen & Ming-Yung Lee & Cheng-Hsun Wu, 2022. "Applications of an extended geometric Brownian motion degradation model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(7), pages 2139-2153, April.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:7:p:2139-2153
    DOI: 10.1080/03610926.2020.1764039
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1764039
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1764039?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.

    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:taf:lstaxx:v:51:y:2022:i:7:p:2139-2153. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.