Abstract:
A variance decomposition approach to quantify the effects of stochastic variables in nonlinear-dynamic models is developed. The decomposition is taken temporally with respect to the source of disturbance. The methodology uses Monte-Carlo methods to estimate the variance decomposition using the ANOVA-like procedures proposed in [G.E.B. Archer, A. Saltelli, I.M. Sobol, Sensitivity measures, anova-like techniques and the use of bootstrap, Journal of Statistical Computation and Simulation 58 (1997) 99–120; A. Saltelli, S. Tarantola, K. Chan, A quantitative model-independent method for global sensitivity analysis of model output, Technometrics 41 (1999) 39–56]. The results in this paper generalize the variance decomposition results that are obtained analytically for linear systems.