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Monte Carlo estimators for small sensitivity indices
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Published/Copyright:
February 13, 2008
Abstract
The standard Monte Carlo algorithm for estimating global sensitivity indices may be spoilt by loss of accuracy if the index is very small. Two approaches were proposed for eliminating the loss of accuracy: reduction of the mean value and correlated sampling. In the present paper both approaches are investigated and a third combined approach is suggested.
Received: 2007-05-02
Revised: 2007-07-15
Published Online: 2008-02-13
Published in Print: 2008-01
© de Gruyter 2007
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- Skewed distributions generated by the Student's t kernel
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Keywords for this article
Sensitivity analysis;
global sensitivity index;
Monte Carlo method;
variance estimates
Articles in the same Issue
- The weighted variance minimization for options pricing
- A quasilinear stochastic partial differential equation driven by fractional white noise
- A quasi-stochastic simulation of the general dynamics equation for aerosols
- Skewed distributions generated by the Student's t kernel
- Expansion of random boundary excitations for elliptic PDEs
- Monte Carlo estimators for small sensitivity indices
- A use of algorithms for numerical modeling of order statistics
