Rank and Linear Correlation Differences in Monte Carlo Simulation
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 1
Abstract
Monte Carlo simulation is used to quantify and characterize uncertainty in a variety of applications, such as cost and engineering economic analysis and project management. The dependence or correlation between the random variables modeled can also be simulated to add more accuracy to simulations. However, there exists a difference between how correlation is most often estimated from data (linear correlation) and the correlation that is simulated (rank correlation). In this research an empirical methodology is developed to estimate the difference between the specified linear correlation between two random variables and the resulting linear correlation when rank correlation is simulated. It is shown that in some cases there can be relatively large differences. The methodology is based on the shape of the quantile-quantile plot of two distributions, the maximum possible linear correlation between two distributions, and the estimated level of correlation between the two random variables. This methodology also gives users the ability to estimate the rank correlation that, when simulated, generates a desired linear correlation. This methodology enhances the accuracy of simulations with dependent random variables while utilizing existing simulation software tools.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 1 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jun 5, 2020
Accepted: Oct 13, 2020
Published online: Dec 17, 2020
Published in print: Mar 1, 2021
Discussion open until: May 17, 2021
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