IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v54y2025i9p2773-2790.html
   My bibliography  Save this article

Bayesian generalized likelihood ratio chart for Gaussian process variance with an application to monitoring hard-bake processes

Author

Listed:
  • Hongxing Cai
  • Amitava Mukherjee
  • Wei Yang
  • Jiujun Zhang

Abstract

Nowadays, Bayesian methods have gained popularity among quality control researchers. Before this study, only KazemiNia, Sadeghpour Gildeh, and Abbasi Ganji (2021) proposed the Bayesian generalized likelihood ratio (BGLR) control chart to monitor the mean of a normal process. In this study, we proposed the BGLR control chart, the Bayesian cumulative sum (BCUSUM) control chart, and the combination of two Bayesian CUSUM (2BCUSUM) control charts to monitor the variance of a normal distribution process. The performance of these charts is evaluated in terms of the steady-state average time to signal. From the overall monitoring effect, with prior information known, the detection capability of the BCUSUM control chart is the best among the three control charts in terms of the results of the Monte Carlo simulation. Furthermore, an application of the proposed charts in monitoring the hard-bake process is discussed to illustrate its implementation design.

Suggested Citation

  • Hongxing Cai & Amitava Mukherjee & Wei Yang & Jiujun Zhang, 2025. "Bayesian generalized likelihood ratio chart for Gaussian process variance with an application to monitoring hard-bake processes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(9), pages 2773-2790, May.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:9:p:2773-2790
    DOI: 10.1080/03610926.2024.2434941
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2024.2434941
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2024.2434941?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:54:y:2025:i:9:p:2773-2790. 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.