IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v39y1993i9p1108-1111.html
   My bibliography  Save this article

Notes: Conditions for the Applicability of the Regenerative Method

Author

Listed:
  • Peter W. Glynn

    (Department of Operations Research, Stanford University, Stanford, California 94305)

  • Donald L. Iglehart

    (Department of Operations Research, Stanford University, Stanford, California 94305)

Abstract

The regenerative method for estimating steady-state parameters is one of the basic methods in simulation output analysis. This method depends on central limit theorems for regenerative processes and weakly consistent estimates for the variance constants arising in the central limit theorems. A weak sufficient condition for both the central limit theorems and consistent estimates is given. Previous authors have implicitly made stronger moment assumptions which have led to strongly consistent variance estimates, more than is needed for the regenerative method to hold. The relationship between conditions for the validity of the regenerative method and those for the validity of standardized time series methods is also discussed.

Suggested Citation

  • Peter W. Glynn & Donald L. Iglehart, 1993. "Notes: Conditions for the Applicability of the Regenerative Method," Management Science, INFORMS, vol. 39(9), pages 1108-1111, September.
  • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:9:p:1108-1111
    DOI: 10.1287/mnsc.39.9.1108
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.39.9.1108
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.39.9.1108?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. Irina Peshkova & Evsey Morozov & Michele Pagano, 2024. "Regenerative Analysis and Approximation of Queueing Systems with Superposed Input Processes," Mathematics, MDPI, vol. 12(14), pages 1-22, July.
    2. T. P. I. Ahamed & V. S. Borkar & S. Juneja, 2006. "Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation," Operations Research, INFORMS, vol. 54(3), pages 489-504, June.
    3. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    4. Shane G. Henderson & Peter W. Glynn, 1999. "Derandomizing Variance Estimators," Operations Research, INFORMS, vol. 47(6), pages 907-916, December.

    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:inm:ormnsc:v:39:y:1993:i:9:p:1108-1111. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.