IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v192y2009i2p468-478.html
   My bibliography  Save this article

A matching algorithm for generation of statistically dependent random variables with arbitrary marginals

Author

Listed:
  • Ilich, Nesa

Abstract

Simulation has gained acceptance in the operations research community as a viable method for analyzing complex problems. While random generation of variables with various marginal distributions has been studied at length, developing ability to preserve a given degree of statistical dependence among them has been lagging behind. This paper includes a short summary of the previous work and a description of the proposed algorithm for efficient re-arranging of generated random variables such that a desired product moment correlation matrix is induced. The proposed approach is different from similar algorithms that induce a desired rank-order correlation among random variables. The algorithm is demonstrated using three numerical examples, one of which also includes a comparison with @RISK commercial package. Its main features are simplicity, ease of implementation and the ability to handle either theoretical or empirical distribution functions.

Suggested Citation

  • Ilich, Nesa, 2009. "A matching algorithm for generation of statistically dependent random variables with arbitrary marginals," European Journal of Operational Research, Elsevier, vol. 192(2), pages 468-478, January.
    • Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:468-478
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(07)00954-X
    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. Soumyadip Ghosh & Shane G. Henderson, 2002. "Chessboard Distributions and Random Vectors with Specified Marginals and Covariance Matrix," Operations Research, INFORMS, vol. 50(5), pages 820-834, October.
    2. Philip M. Lurie & Matthew S. Goldberg, 1998. "An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions," Management Science, INFORMS, vol. 44(2), pages 203-218, February.
    3. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    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. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    2. Đurica Marković & Jasna Plavšić & Nesa Ilich & Siniša Ilić, 2015. "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4787-4801, October.

    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. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    2. I-Tung Yang, 2006. "Using Gaussian copula to simulate repetitive projects," Construction Management and Economics, Taylor & Francis Journals, vol. 24(9), pages 901-909.
    3. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    4. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    5. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    6. A. E. Ades & G. Lu, 2003. "Correlations Between Parameters in Risk Models: Estimation and Propagation of Uncertainty by Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1165-1172, December.
    7. Tianyang Wang & James S. Dyer, 2012. "A Copulas-Based Approach to Modeling Dependence in Decision Trees," Operations Research, INFORMS, vol. 60(1), pages 225-242, February.
    8. Stanhope, Stephen, 2005. "Case studies in multivariate-to-anything transforms for partially specified random vector generation," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 68-79, August.
    9. Huifen Chen, 2001. "Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 312-331, November.
    10. Soumyadip Ghosh & Shane G. Henderson, 2002. "Chessboard Distributions and Random Vectors with Specified Marginals and Covariance Matrix," Operations Research, INFORMS, vol. 50(5), pages 820-834, October.
    11. Ho-Yin Mak & Zuo-Jun Max Shen, 2014. "Pooling and Dependence of Demand and Yield in Multiple-Location Inventory Systems," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 263-269, May.
    12. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    13. Alexei Alexandrov & Özlem Bedre-Defolie, 2014. "The Equivalence of Bundling and Advance Sales," Marketing Science, INFORMS, vol. 33(2), pages 259-272, March.
    14. Charles J. Corbett & Kumar Rajaram, 2006. "A Generalization of the Inventory Pooling Effect to Nonnormal Dependent Demand," Manufacturing & Service Operations Management, INFORMS, vol. 8(4), pages 351-358, August.
    15. Benoumechiara Nazih & Bousquet Nicolas & Michel Bertrand & Saint-Pierre Philippe, 2020. "Detecting and modeling critical dependence structures between random inputs of computer models," Dependence Modeling, De Gruyter, vol. 8(1), pages 263-297, January.
    16. Jorge A. Sefair & Oscar Guaje & Andrés L. Medaglia, 2021. "A column-oriented optimization approach for the generation of correlated random vectors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 777-808, September.
    17. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    18. Jason R. W. Merrick, 2008. "Getting the Right Mix of Experts," Decision Analysis, INFORMS, vol. 5(1), pages 43-52, March.
    19. Pham, Linh & Nguyen, Canh Phuc, 2021. "Asymmetric tail dependence between green bonds and other asset classes," Global Finance Journal, Elsevier, vol. 50(C).
    20. Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, 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:eee:ejores:v:192:y:2009:i:2:p:468-478. 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.elsevier.com/locate/eor .

    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.