Skip to main content
Log in

Iterative estimation of Sobol’ indices based on replicated designs

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

In the field of sensitivity analysis, Sobol’ indices are widely used to assess the importance of the inputs of a model to its output. Among the methods that estimate these indices, the replication procedure is noteworthy for its efficient cost. A practical problem is how many model evaluations must be performed to guarantee a sufficient precision on the Sobol’ estimates. The present paper tackles this issue by rendering the replication procedure iterative. The idea is to enable the addition of new model evaluations to progressively increase the accuracy of the estimates. These evaluations are done at points located in under-explored regions of the experimental designs, but preserving their characteristics. The key feature of this approach is the construction of nested space-filling designs. For the estimation of first-order indices, a nested Latin hypercube design is used. For the estimation of closed second-order indices, two constructions of a nested orthogonal array design are proposed. Regularity and uniformity properties of the nested designs are studied.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Bratley P, Niederreiter BL (1992) Implementation and tests of low-discrepancy sequences. ACM Trans Model Comput Simul 2(3):195–213. https://doi.org/10.1145/146382.146385

    Article  MATH  Google Scholar 

  • Devroye L (1986) Non-uniform random variate generation. Springer, New York

    Book  Google Scholar 

  • Dey A (2012) On the construction of nested orthogonal arrays. Australas J Combin 54:37–48

    MathSciNet  MATH  Google Scholar 

  • Franco J, Vasseur O, Corre N, Sergent M (2009) Minimum spanning tree: a new approach to assess the quality of the design of computer experiments. Chemom Intell Lab Syst 97(2):164–169. https://doi.org/10.1016/j.chemolab.2009.03.011

    Article  Google Scholar 

  • Gilquin L, Rugama LAJ, Arnaud E, Hickernell FJ, Monod H, Prieur C (2017) Iterative construction of replicated designs based on Sobol’ sequences. C R Math 355(1):10–14

    Article  MathSciNet  Google Scholar 

  • Hedayat AS, Sloane NJA, Stufken J (1999) Orthogonal arrays: theory and applications, Springer series in statistic. Springer, New York

    Book  Google Scholar 

  • Hoeffding W (1948) A class of statistics with asymptotically normal distributions. Ann Math Stat 19(3):293–325

    Article  MathSciNet  Google Scholar 

  • Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52:1–17

    Article  Google Scholar 

  • Janon A, Klein T, Lagnoux A, Nodet M, Prieur C (2014) Asymptotic normality and efficiency of two Sobol’ index estimators. ESAIM Probab Stat 18:342–364. https://doi.org/10.1051/ps/2013040

    Article  MathSciNet  MATH  Google Scholar 

  • Jonshon ME, Moore LM, Ylvisaker D (1990) Minmax and maxi-min distance designs. J Stat Plan Inference 26(2):131s–148. https://doi.org/10.1016/0378-3758(90)90122-B

    Article  Google Scholar 

  • Mara TA, Joseph OR (2008) Comparison of some efficient met-hods to evaluate the main effect of computer model factors. J Stat Comput Simul 78(2):167–178. https://doi.org/10.1080/10629360600964454

    Article  MathSciNet  MATH  Google Scholar 

  • McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245. https://doi.org/10.2307/1271432

    Article  MathSciNet  MATH  Google Scholar 

  • Monod H, Naud C, Makowski D (2006) Uncertainty and sensitivity analysis for crop models. Working with dynamic crop models. Elsevier, New York, pp 55–100

    Google Scholar 

  • Morokoff WJ, Caflisch RE (1994) Quasi-random sequences and their discrepancies. SIAM J Sci Comput 15(16):1251–1279

    Article  MathSciNet  Google Scholar 

  • Prieur C, Tarantola S (2017) Variance-based sensitivity analysis: theory and estimation algorithms. Handbook of uncertainty quantification, pp 1217–1239

  • Qian PZG (2009) Nested Latin hypercube designs. Biometrika 96(4):957–970. https://doi.org/10.1093/biomet/asp045

    Article  MathSciNet  MATH  Google Scholar 

  • Qian PZG, Ai M, Wu CFJ (2009a) Construction of nested space-filling designs. Ann Stat 37(6A):3616–3643. https://doi.org/10.1214/09-AOS690

    Article  MathSciNet  MATH  Google Scholar 

  • Qian PZG, Tang B, Wu CFJ (2009b) Nested space-filling designs for computer experiments with two levels of accuracy. Stat Sin 19:287–300

    MathSciNet  MATH  Google Scholar 

  • Rennen G, Husslage B, Van Dam ER, Den Hertog D (2010) Nested maximin latin hypercube designs. Struct Multidiscip Optim 41(3):371–395

    Article  MathSciNet  Google Scholar 

  • Sallaberry CJ, Helton JC, Hora SC (2008) Extension of latin hypercube samples with correlated variables. Reliab Eng Syst Saf 93(7):1047–1059

    Article  Google Scholar 

  • Saltelli A (2002) Making best use of models evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297

    Article  MathSciNet  Google Scholar 

  • Saltelli A, Chan K, Scott EM (2008) Sensitivity analysis. Wiley, New York

    MATH  Google Scholar 

  • Sarrazin F, Pianosi F, Wagener T (2016) Global sensitivity analysis of environmental models: convergence and validation. Environ Modell Softw 79:135–152

    Article  Google Scholar 

  • Sheikholeslami R, Razavi S, Gupta HV, Becker W, Haghnegahdar A (2019) Global sensitivity analysis for high-dimensional problems: how to objectively group factors and measure robustness and convergence while reducing computational cost. Environ Model Softw 111:282–299

    Article  Google Scholar 

  • Sobol’ IM (1993) Sensitivity indices for nonlinear mathematical models. Math Model Comput Exp 1:407–414

    MATH  Google Scholar 

  • Stinson DR, Massey JL (1995) An infinite class of counterexamples to a conjecture concerning nonlinear resilient functions. J Cryptol 8:67–173. https://doi.org/10.1007/BF00202271

    Article  MathSciNet  MATH  Google Scholar 

  • Terraz T, Ribes A, Fournier Y, Iooss B, Raffin B (2017) Melissa: large scale in transit sensitivity analysis avoiding intermediate files. In: Proceedings of the international conference for high performance computing, networking, storage and analysis, pp 1–14

  • Tissot JY, Prieur C (2015) A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol’ indices. J Stat Comput Simul 85:1358–1381. https://doi.org/10.1080/00949655.2014.971799

    Article  MathSciNet  MATH  Google Scholar 

  • Vorechovsky M, Novak D, Rusina R (2013) Sample size extension in stratified sampling: Theory and software implementation. In: Safety, reliability, risk and life-cycle performance of structures and infrastructures: proceedings of the 11th international conference on structural safety and reliability (ICOSSAR 2013), IASSAR. CRC Press, New York

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Clémentine Prieur.

Additional information

Communicated by Eduardo Souza de Cursi.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gilquin, L., Prieur, C., Arnaud, E. et al. Iterative estimation of Sobol’ indices based on replicated designs. Comp. Appl. Math. 40, 18 (2021). https://doi.org/10.1007/s40314-020-01402-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40314-020-01402-5

Keywords

Mathematics Subject Classification

Navigation