Global sensitivity analysis using complex linear models

Authors:
Astrid Jourdan
Affiliations:
Département de Mathématiques, E.I.S.T.I., Pau cedex 9, France 64062
Venue:
Statistics and Computing
Year:
2012

Citing 4
Cited 0

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A quantitative model-independent method for global sensitivity analysis of model output

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Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)

Design and Modeling for Computer Experiments (Computer Science & Data Analysis)

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Abstract

A global sensitivity analysis of complex computer codes is usually performed by calculating the Sobol indices. The indices are estimated using Monte Carlo methods. The Monte Carlo simulations are time-consuming even if the computer response is replaced by a metamodel. This paper proposes a new method for calculating sensitivity indices that overcomes the Monte Carlo estimation. The method assumes a discretization of the domain of simulation and uses the expansion of the computer response on an orthogonal basis of complex functions to built a metamodel. This metamodel is then used to derive an analytical estimation of the Sobol indices. This approach is successfully tested on analytical functions and is compared with two alternative methods.