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

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:203-218
    DOI: 10.1287/mnsc.44.2.203
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    References listed on IDEAS

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    1. Parrish, Rudolph S., 1990. "Generating random deviates from multivariate Pearson distributions," Computational Statistics & Data Analysis, Elsevier, vol. 9(3), pages 283-295, May.
    2. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
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