IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

An Application of Copulas to Accident Precursor Analysis

Listed author(s):
  • Woojune Yi

    (System and Communication Research Laboratory, Korea Electric Power Research Institute, Taejeon, Korea)

  • Vicki M. Bier

    (Department of Industrial Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706)

Registered author(s):

    Data on accident precursors can help in estimating accident frequencies, since they provide a rich source of information on intersystem dependencies. However, Bayesian analysis of accident precursors requires the ability to construct joint prior distributions reflecting such dependencies. For example, the failure probabilities of a particular safety system under normal and accident conditions, respectively, will generally not be identical (because of the effects of the accident), but will almost certainly be correlated (since both failure probabilities reflect the performance of the same components, with the same inherent levels of reliability). In this paper, we explore the use of copulas (a method of representing joint distribution functions with particular marginals) to construct the needed prior distributions, and then use these distributions in a Bayesian analysis of hypothetical precursor data. This demonstrates the usefulness of copulas in practice. The same approach can also be used in a wide variety of other contexts where joint distributions with particular marginals are desired.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 12-Part-2 (December)
    Pages: 257-270

    in new window

    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:12-part-2:p:s257-s270
    Contact details of provider: Postal:
    7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA

    Phone: +1-443-757-3500
    Fax: 443-757-3515
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Bier, Vicki M. & Yi, Woojune, 1995. "A Bayesian method for analyzing dependencies in precursor data," International Journal of Forecasting, Elsevier, vol. 11(1), pages 25-41, March.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:44:y:1998:i:12-part-2:p:s257-s270. 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: (Mirko Janc)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.