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An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions

  • Philip M. Lurie

    (Institute for Defense Analyses, 1801 N. Beauregard Street, Alexandria, Virginia 22311-1772)

  • Matthew S. Goldberg

    (Institute for Defense Analyses, 1801 N. Beauregard Street, Alexandria, Virginia 22311-1772)

Registered author(s):

    This paper presents an algorithm for generating correlated vectors of random numbers. The user need not fully specify the joint distribution function; instead, the user "partially specifies" only the marginal distributions and the correlation matrix. The algorithm may be applied to any set of continuous, strictly increasing distribution functions; the marginal distributions need not all be of the same functional form. The correlation matrix is first checked for mathematical consistency (positive semi-definiteness), and adjusted if necessary. Then the correlated random vectors are generated using a combination of Cholesky decomposition and Gauss-Newton iteration. Applications are made to cost analysis, where correlations are often present between cost elements in a work breakdown structure.

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    File URL: http://dx.doi.org/10.1287/mnsc.44.2.203
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 2 (February)
    Pages: 203-218

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    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:203-218
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