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Monte Carlo evaluation of derivative-based global sensitivity measures
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References

  • 1991Factorial Sampling Plans for Preliminary Computational Experiments4,936 cited
  • 2007An effective screening design for sensitivity analysis of large models2,024 cited
  • 1996Importance measures in global sensitivity analysis of nonlinear models2,014 cited
  • 2002Sensitivity Analysis in Practice526 cited
  • 1995About the use of rank transformation in sensitivity analysis of model output340 cited

Cited by

  • 2020Sensitivity analysis methods in the biomedical sciences168 cited
  • 2017Global sensitivity metrics from active subspaces158 cited
  • 2012Efficient sampling methods for global reliability sensitivity analysis157 cited
  • 2013Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties155 cited
  • 2013Derivative-based global sensitivity measures: General links with Sobol’ indices and numerical tests135 cited
  • 2019A Moment Approach to Positioning Accuracy Reliability Analysis for Industrial Robots132 cited
  • 2010A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices124 cited
  • 2014Computing derivative-based global sensitivity measures using polynomial chaos expansions116 cited
  • 2019Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications

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94 cited
  • 2012Update 1 of: Sensitivity Analysis for Chemical Models92 cited
  • 2011Global sensitivity analysis using sparse grid interpolation and polynomial chaos81 cited
  • 2017Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs81 cited
  • 2010Derivative based global sensitivity measures65 cited
  • 2016Global sensitivity analysis using a Gaussian Radial Basis Function metamodel59 cited
  • 2012A derivative based sensitivity measure of failure probability in the presence of epistemic and aleatory uncertainties55 cited
  • 2009A backoff strategy for model‐based experiment design under parametric uncertainty55 cited