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Screening important inputs in models with strong interaction properties

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  • Saltelli, Andrea
  • Campolongo, Francesca
  • Cariboni, Jessica

Abstract

We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs. The method proposed here represents an improvement over the best available practice for this setting when dealing with models having strong interaction effects. When the sample size is sufficiently high the same design can also be used to obtain accurate quantitative estimates of the variance-based sensitivity measures: the same simulations can be used to obtain estimates of the variance-based measures according to the Sobol’ and the Jansen formulas. Results demonstrate that Sobol’ is more efficient for the computation of the first-order indices, while Jansen performs better for the computation of the total indices.

Suggested Citation

  • Saltelli, Andrea & Campolongo, Francesca & Cariboni, Jessica, 2009. "Screening important inputs in models with strong interaction properties," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1149-1155.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:7:p:1149-1155
    DOI: 10.1016/j.ress.2008.10.007
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    References listed on IDEAS

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    1. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    2. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
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    3. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
    4. Borgonovo, Emanuele & Rabitti, Giovanni, 2023. "Screening: From tornado diagrams to effective dimensions," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1200-1211.
    5. Chu-Agor, M.L. & Muñoz-Carpena, R. & Kiker, G.A. & Aiello-Lammens, M.E. & Akçakaya, H.R. & Convertino, M. & Linkov, I., 2012. "Simulating the fate of Florida Snowy Plovers with sea-level rise: Exploring research and management priorities with a global uncertainty and sensitivity analysis perspective," Ecological Modelling, Elsevier, vol. 224(1), pages 33-47.
    6. Hanqing Ma & Chunfeng Ma & Xin Li & Wenping Yuan & Zhengjia Liu & Gaofeng Zhu, 2020. "Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
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    8. Emanuele Borgonovo, 2010. "A Methodology for Determining Interactions in Probabilistic Safety Assessment Models by Varying One Parameter at a Time," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 385-399, March.
    9. Xu, Chonggang & Gertner, George, 2011. "Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 184-198, January.
    10. Marzloff, Martin P. & Johnson, Craig R. & Little, L. Rich & Soulié, Jean-Christophe & Ling, Scott D. & Frusher, Stewart D., 2013. "Sensitivity analysis and pattern-oriented validation of TRITON, a model with alternative community states: Insights on temperate rocky reefs dynamics," Ecological Modelling, Elsevier, vol. 258(C), pages 16-32.
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