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

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.

• 저널Management Science · 44(2) · 203–218
• 토픽Scientific Research and Discoveries · Statistical and Nonlinear Physics
• DOI10.1287/mnsc.44.2.203
• 저자Philip M. Lurie, Matthew S. Goldberg