A Non-simulation Based Method for Inducing Pearson's Correlation between Input Random Variables
Loading...
Authors
Druker, Eric R.
Coleman, Richard L.
Braxton, Peter J.
Subjects
Managing Risk
Risk-analysis Models, Pearson''s Correlation between Input/Independent Random Variables
Risk-analysis Models, Pearson''s Correlation between Input/Independent Random Variables
Advisors
Date of Issue
2008-04-01
Date
01-Apr-08
Publisher
Language
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.
Type
Technical Report
Description
Proceedings Paper (for Acquisition Research Program)
Series/Report No
Department
Acquisition Management
Other Research Faculty
Identifiers
NPS Report Number
NPS-AM-08-041
Sponsors
Naval Postgraduate School Acquisition Research Program
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.