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Multivariate stochastic simulation with subjective multivariate normal distributionsAuthor(s): P. J. Ince; J. Buongiorno
Source: Proceedings of the 1991 Symposium on Systems Analysis in Forest Resources : March 3-6, 1991, Charleston, South Carolina. Asheville, NC : Southeastern Forest Experiment Station, 1991. General technical report SE ; 74.:p. 143-150 : ill.
Publication Series: General Technical Report (GTR)
Station: Forest Products Laboratory
PDF: Download Publication (87.0 MB)
DescriptionIn many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal distributions. The method requires subjective estimates of the 99-percent confidence interval for the expected value of each random variable and of the partial correlations among the variables. The technique can be used to generate pseudorandom data corresponding to the specified distribution. If the subjective parameters do not yield a positive definite covariance matrix, the technique determines minimal adjustments in variance assumptions needed to restore positive definiteness. The method is validated and then applied to a capital investment simulation for a new papermaking technology. In that example, with ten correlated random variables, no significant difference was detected between multivariate stochastic simulation results and results that ignored the correlation. In general, however, data correlation could affect results of stochastic simulation, as shown by the validation results.
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CitationInce, P. J.; Buongiorno, J. 1991. Multivariate stochastic simulation with subjective multivariate normal distributions. Proceedings of the 1991 Symposium on Systems Analysis in Forest Resources : March 3-6, 1991, Charleston, South Carolina. Asheville, NC : Southeastern Forest Experiment Station, 1991. General technical report SE ; 74.:p. 143-150 : ill.
KeywordsForestry, Forest products industries, Monte Carlo method, Stochastic processes, Mathematical models, Statistical analysis
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