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Randomized quasi-Monte Carlo methods in global sensitivity analysis

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  • Ökten, Giray
  • Liu, Yaning

Abstract

Randomized quasi-Monte Carlo methods have enjoyed increasing popularity in applications due to their faster convergence rate than Monte Carlo, and the existence of simple statistical tools to analyze the error of their estimates similar to Monte Carlo. In this paper we give a survey of randomized quasi-Monte Carlo methods, transformation methods for low-discrepancy sequences, and provide some examples.

Suggested Citation

  • Ökten, Giray & Liu, Yaning, 2021. "Randomized quasi-Monte Carlo methods in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000818
    DOI: 10.1016/j.ress.2021.107520
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    References listed on IDEAS

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    1. Liu, Yaning & Yousuff Hussaini, M. & Ökten, Giray, 2016. "Accurate construction of high dimensional model representation with applications to uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 281-295.
    2. Linlin Xu & Giray Ökten, 2015. "High-performance financial simulation using randomized quasi-Monte Carlo methods," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1425-1436, August.
    3. Liu, Ruixue & Owen, Art B., 2006. "Estimating Mean Dimensionality of Analysis of Variance Decompositions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 712-721, June.
    4. Kucherenko, S. & Song, S., 2017. "Different numerical estimators for main effect global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 222-238.
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    Cited by:

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