On the Relative Importance of Input Factors in Mathematical Models

@article{Saltelli2002OnTR,
  title={On the Relative Importance of Input Factors in Mathematical Models},
  author={A. Saltelli and S. Tarantola},
  journal={Journal of the American Statistical Association},
  year={2002},
  volume={97},
  pages={702 - 709}
}
  • A. Saltelli, S. Tarantola
  • Published 2002
  • Mathematics
  • Journal of the American Statistical Association
  • This article deals with global quantitative sensitivity analysis of the Level E model, a computer code used in safety assessment for nuclear waste disposal. The Level E code has been the subject of two international benchmarks of risk assessment codes and Monte Carlo methods and is well known in the literature. We discuss the Level E model with reference to two different settings. In the first setting, the objective is to find the input factor that drives most of the output variance. In the… CONTINUE READING
    Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
    • 2,115
    • PDF
    An effective screening design for sensitivity analysis of large models
    • 1,153
    • PDF
    A new uncertainty importance measure
    • 542
    • Highly Influenced
    • PDF
    How to avoid a perfunctory sensitivity analysis
    • 655
    • PDF
    Probabilistic sensitivity analysis of complex models: a Bayesian approach
    • 874
    • PDF
    A Review on Global Sensitivity Analysis Methods
    • 405
    • Highly Influenced
    • PDF
    Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators
    • 501
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 32 REFERENCES
    Importance measures in global sensitivity analysis of nonlinear models
    • 1,278
    • PDF
    Making best use of model evaluations to compute sensitivity indices
    • 1,117
    A distribution-free approach to inducing rank correlation among input variables
    • 1,467
    Sensitivity Analysis as an Ingredient of Modeling
    • 588
    • PDF
    Screening, predicting, and computer experiments
    • 608
    Sensitivity analysis of model output: an investigation of new techniques
    • 206
    • PDF
    A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
    • 377
    • PDF
    About the use of rank transformation in sensitivity analysis of model output
    • 291
    • PDF
    An importance quantification technique in uncertainty analysis for computer models
    • 197
    Evaluating Prediction Uncertainty
    • 168
    • PDF