A Non-Simulation Based Method for Inducing Pearson's Correlation Between Input Random Variables

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.

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Document Details

Document Type
Technical Report
Publication Date
Apr 23, 2008
Accession Number
ADA493990

Entities

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  • Eric R. Druker
  • Peter J. Braxton
  • Richard L. Coleman

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  • Energy and Power Technologies
  • Ground and Sea Platforms

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  • Algorithms
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