IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v26y1999i2p273-292.html
   My bibliography  Save this article

Statistical process control and model monitoring

Author

Listed:
  • P. J. Harrison

Abstract

This paper is concerned with model monitoring and quality control schemes, which are founded on a decision theoretic formulation. After identifying unacceptable weaknesses associated with Wald, sequential probability ratio test (SPRT) and Cuscore monitors, the Bayes decision monitor is developed. In particular, the paper focuses on what is termed a 'popular decision scheme' (PDS) for which the monitoring run loss functions are specified simply in terms of two indiff erence qualities. For most applications, the PDS results in forward cumulative sum tests of functions of the observations. For many exponential family applications, the PDS is equivalent to well-used SPRTs and Cusums. In particular, a neat interpretation of V-mask cusum chart settings is derived when simultaneously running two symmetric PDSs. However, apart from providing a decision theoretic basis for monitoring, sensible procedures occur in applications for which SPRTs and Cuscores are particularly unsatisfactory. Average run lengths (ARLs) are given for two special cases, and the inadequacy of the Wald and similar ARL approximations is revealed. Generalizations and applications to normal and dynamic linear models are discussed. The paper concludes by deriving conditions under which sequences of forward and backward sequential or Cusum chart tests are equivalent.

Suggested Citation

  • P. J. Harrison, 1999. "Statistical process control and model monitoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(2), pages 273-292.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:273-292
    DOI: 10.1080/02664769922601
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922601
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664769922601?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.

    References listed on IDEAS

    as
    1. A. F. Bissell, 1969. "Cusum Techniques for Quality Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 18(1), pages 1-25, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Douglas M. Hawkins & F. Lombard, 2017. "Cusum control for data following the von Mises distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1319-1332, June.
    2. Guanfu Liu & Xiaolong Pu & Lei Wang & Dongdong Xiang, 2015. "CUSUM chart for detecting range shifts when monotonicity of likelihood ratio is invalid," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1635-1644, August.
    3. Salvador, Manuel & Gargallo, Pilar, 2004. "Automatic monitoring and intervention in multivariate dynamic linear models," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 401-431, October.
    4. Manuel Salvador & Pilar Gargallo, 2003. "Automatic selective intervention in dynamic linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1161-1184.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fu, James C. & Spiring, Fred A. & Xie, Hansheng, 2002. "On the average run lengths of quality control schemes using a Markov chain approach," Statistics & Probability Letters, Elsevier, vol. 56(4), pages 369-380, February.
    2. Luceno, Alberto, 1999. "Average run lengths and run length probability distributions for cuscore charts to control normal mean," Computational Statistics & Data Analysis, Elsevier, vol. 32(2), pages 177-195, December.
    3. Pospisil, Libor & Vecer, Jan & Hadjiliadis, Olympia, 2009. "Formulas for stopped diffusion processes with stopping times based on drawdowns and drawups," Stochastic Processes and their Applications, Elsevier, vol. 119(8), pages 2563-2578, August.

    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:26:y:1999:i:2:p:273-292. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.