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.
Volume (Year): 44 (1998)
Issue (Month): 2 (February)
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- Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
- Parrish, Rudolph S., 1990. "Generating random deviates from multivariate Pearson distributions," Computational Statistics & Data Analysis, Elsevier, vol. 9(3), pages 283-295, May.
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