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

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  • Ilich, Nesa
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6VCT-4PRRBM8-4/2/1e12629e0f580a42de48d68d2d37fb43
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 192 (2009)
    Issue (Month): 2 (January)
    Pages: 468-478

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    Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:468-478

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Simulation Regression Stochastic processes Statistical dependence Correlation;

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    1. 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.
    2. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
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