IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v110y2016icp137-145.html
   My bibliography  Save this article

Scan statistics for detecting a local change in variance for normal data with unknown population variance

Author

Listed:
  • Zhao, Bo
  • Glaz, Joseph

Abstract

In this article we investigate the performance of fixed, multiple and variable window scan statistics in detecting a local change in variance for a sequence of normal observations, when the population variance of the underlying normal distribution is unknown. For fixed window scan statistics, we study: the training sample approach, the conditioning on sufficient statistic approach and the parametric bootstrap testing approach. It is evident from the numerical results that the scan statistic constructed via the conditioning approach outperforms the other two fixed window scan statistics investigated in this article. Based on power calculation presented in this article, one can conclude that when the size of the window where a local change of variance has occurred is unknown, multiple and variable window scan statistics outperform fixed window scan statistics. The multiple and variable window scan statistics perform equally well. When the sequence of observations is large, the implementation of the multiple window scan statistic is computationally more practical.

Suggested Citation

  • Zhao, Bo & Glaz, Joseph, 2016. "Scan statistics for detecting a local change in variance for normal data with unknown population variance," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 137-145.
  • Handle: RePEc:eee:stapro:v:110:y:2016:i:c:p:137-145
    DOI: 10.1016/j.spl.2015.12.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016771521500406X
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Loredana Ureche-Rangau & Franck Speeg, 2011. "A simple method for variance shift detection at unknown time points," Economics Bulletin, AccessEcon, vol. 31(3), pages 2204-2218.
    2. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    3. Andreu Sansó & Vicent Aragó & Josep Lluís Carrion, 2003. "Testing for Changes in the Unconditional Variance of Financial Time Series," DEA Working Papers 5, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    4. Wang, Xiao & Zhao, Bo & Glaz, Joseph, 2014. "A multiple window scan statistic for time series models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 196-203.
    Full references (including those not matched with items on IDEAS)

    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:eee:stapro:v:110:y:2016:i:c:p:137-145. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.