IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v29y2002i4p693-719.html
   My bibliography  Save this article

Truncated Sequential Change‐point Detection based on Renewal Counting Processes

Author

Listed:
  • ALLAN GUT
  • JOSEF STEINEBACH

Abstract

The typical approach in change‐point theory is to perform the statistical analysis based on a sample of fixed size. Alternatively, one observes some random phenomenon sequentially and takes action as soon as one observes some statistically significant deviation from the “normal” behaviour. Based on the, perhaps, more realistic situation that the process can only be partially observed, we consider the counting process related to the original process observed at equidistant time points, after which action is taken or not depending on the number of observations between those time points. In order for the procedure to stop also when everything is in order, we introduce a fixed time horizon n at which we stop declaring “no change” if the observed data did not suggest any action until then. We propose some stopping rules and consider their asymptotics under the null hypothesis as well as under alternatives. The main basis for the proofs are strong invariance principles for renewal processes and extreme value asymptotics for Gaussian processes.

Suggested Citation

  • Allan Gut & Josef Steinebach, 2002. "Truncated Sequential Change‐point Detection based on Renewal Counting Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 693-719, December.
  • Handle: RePEc:bla:scjsta:v:29:y:2002:i:4:p:693-719
    DOI: 10.1111/1467-9469.00313
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9469.00313
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-9469.00313?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
    ---><---

    Citations

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


    Cited by:

    1. Michael Messer & Gaby Schneider, 2017. "The shark fin function: asymptotic behavior of the filtered derivative for point processes in case of change points," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 253-272, July.
    2. Gut, Allan & Steinebach, Josef, 2010. "Asymptotics for increments of stopped renewal processes," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 558-565, April.

    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:bla:scjsta:v:29:y:2002:i:4:p:693-719. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.