Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/86
Title: | A Non-simulation Based Method for Inducing Pearson's Correlation between Input Random Variables |
Authors: | Eric R. Druker Richard L. Coleman Peter J. Braxton |
Keywords: | Risk-Analysis Models Pearson's Correlation Between Input/Independent Random Variables |
Issue Date: | 1-Apr-2008 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Managing Risk NPS-AM-08-041 |
Abstract: | Several previously published papers have cited the need to include correlation in risk-analysis models. In particular, a landmark paper published by Philip Lurie and Matthew Goldberg presented a methodology for inducing Pearson's correlation between input/independent random variables. The one subject, absent from the paper, was a methodology for finding the optimal applied correlation matrix given a desired outcome correlation. Since the publishing of the Lurie-Goldberg paper, there has been continuing discussion on its implementation; however, there has not been any presentation of an optimization algorithm that does not involve the use of computing-heavy simulations. This paper reviews the general methodology used by Lurie and Goldberg (along with its predecessor papers) and presents a non-simulation approach to finding the optimal input correlation matrix, given a set of marginal distributions and a desired correlation matrix. |
Description: | Acquisition Management / Grant-funded Research |
URI: | https://dair.nps.edu/handle/123456789/86 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Size | Format | |
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NPS-AM-08-041.pdf | 111.23 kB | Adobe PDF | View/Open |
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