An approximate method for sampling correlated random variables from partially-specified distributions

被引:81
作者
Lurie, PM [1 ]
Goldberg, MS [1 ]
机构
[1] Inst Def Anal, Alexandria, VA 22311 USA
关键词
simulation; random number generation; correlation; Gauss-Newton method;
D O I
10.1287/mnsc.44.2.203
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
收藏
页码:203 / 218
页数:16
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