Variance decompositions of nonlinear-dynamic stochastic systems Export

@article{citeulike:5826687, 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.}, author = {Harris, T. J. and Yu, W.}, citeulike-article-id = {5826687}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jprocont.2009.08.001}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0959152409001371}, day = {18}, doi = {10.1016/j.jprocont.2009.08.001}, issn = {09591524}, journal = {Journal of Process Control}, keywords = {anova, dynamics, nonlinear, sensitivity, statistics, stochastic, variance}, month = {September}, posted-at = {2009-11-11 18:30:37}, priority = {2}, title = {Variance decompositions of nonlinear-dynamic stochastic systems}, url = {http://dx.doi.org/10.1016/j.jprocont.2009.08.001}, year = {2009} }

TY - JOUR ID - citeulike:5826687 L3 - citeulike-article-id:5826687 N2 - 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. JF - Journal of Process Control SN - 09591524 TI - Variance decompositions of nonlinear-dynamic stochastic systems KW - anova KW - dynamics KW - nonlinear KW - sensitivity KW - statistics KW - stochastic KW - variance AU - Harris, TJ AU - Yu, W PY - 2009/09/18/ UR - http://dx.doi.org/10.1016/j.jprocont.2009.08.001 ER -