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

Philip M. Lurie and Matthew S. Goldberg
Additional contact information
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

Management Science, 1998, vol. 44, issue 2, 203-218

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.

Keywords: Simulation; Random Number Generation; Correlation; Gauss-Newton Method (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.44.2.203 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:44:y:1998:i:2:p:203-218

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().