IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v24y1997i4p475-492.html
   My bibliography  Save this article

Detection capability of residual control chart for stationary process data

Author

Listed:
  • Nien Fan Zhang

Abstract

In recent years, methods for dealing with autocorrelated data in the statistical process control environment have been proposed. A primary method is based on modeling the process data and applying control charts to the residuals. However, the residual charts do not have the same properties as the traditional charts. In the literature, there has been no systematic study on the detection capability of the residual chart for the stationary processes. The article develops a measure of the detection capability of the residual chart for the general stationary processes. Conditions under which the residual chart reduces or increases the detection capability are given. The relationships between the detection capability and the average run length of the residual chart are also established.

Suggested Citation

  • Nien Fan Zhang, 1997. "Detection capability of residual control chart for stationary process data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(4), pages 475-492.
    • Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:475-492
    DOI: 10.1080/02664769723657
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723657
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664769723657?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yaping Li & Haiyan Li & Zhen Chen & Ying Zhu, 2022. "An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations," Energies, MDPI, vol. 15(5), pages 1-13, February.
    2. Zamba, K.D. & Tsiamyrtzis, Panagiotis & Hawkins, Douglas M., 2013. "A three-state recursive sequential Bayesian algorithm for biosurveillance," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 82-97.
    3. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.

    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:japsta:v:24:y:1997:i:4:p:475-492. 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/CJAS20 .

    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.