IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v26y1984i6p502-512.html
   My bibliography  Save this article

On the optimality and efficiency of common random numbers

Author

Listed:
  • Gal, S.
  • Rubinstein, R.Y.
  • Ziv, A.

Abstract

Some theoretical and practical aspects of common random numbers (CRN) for variance reduction in simulation analysis are considered. A simple proof of the optimality of CRN is presented and the efficiency of this technique for variance reduction is discussed. Applications of CRN to production planning and inventory problems while using stochastic approximation are given.

Suggested Citation

  • Gal, S. & Rubinstein, R.Y. & Ziv, A., 1984. "On the optimality and efficiency of common random numbers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 26(6), pages 502-512.
  • Handle: RePEc:eee:matcom:v:26:y:1984:i:6:p:502-512
    DOI: 10.1016/0378-4754(84)90030-2
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0378475484900302
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/0378-4754(84)90030-2?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. R. D. Wright & T. E. Ramsay, Jr., 1979. "On the Effectiveness of Common Random Numbers," Management Science, INFORMS, vol. 25(7), pages 649-656, July.
    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. Ankush Agarwal & Stefano de Marco & Emmanuel Gobet & Gang Liu, 2017. "Rare event simulation related to financial risks: efficient estimation and sensitivity analysis," Working Papers hal-01219616, HAL.
    2. Nathan L. Kleinman & James C. Spall & Daniel Q. Naiman, 1999. "Simulation-Based Optimization with Stochastic Approximation Using Common Random Numbers," Management Science, INFORMS, vol. 45(11), pages 1570-1578, November.

    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. Safizadeh, M. Hossein, 2002. "Minimizing the bias and variance of the gradient estimate in RSM simulation studies," European Journal of Operational Research, Elsevier, vol. 136(1), pages 121-135, January.
    2. Potter, Andrew & Yang, Biao & Lalwani, Chandra, 2007. "A simulation study of despatch bay performance in the steel processing industry," European Journal of Operational Research, Elsevier, vol. 179(2), pages 567-578, June.
    3. Joshi, Shirish & Tew, Jeffrey D., 1995. "Validation and statistical analysis procedures under the common random number correlation-induction strategy for multipopulation simulation experiments," European Journal of Operational Research, Elsevier, vol. 85(1), pages 205-220, August.
    4. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.

    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:eee:matcom:v:26:y:1984:i:6:p:502-512. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.