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A nonparametric importance sampling estimator for moment independent importance measures

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  • Derennes, Pierre
  • Morio, Jérôme
  • Simatos, Florian

Abstract

Moment independent importance measures have been proposed by Borgonovo [1] in order to alleviate some of the drawbacks of variance-based sensibility indices. They have gained increasing attention over the last years but their estimation remains a challenging issue. An effective estimation scheme in the case of correlated inputs, referred to as single-loop method, has been proposed by Wei et al. [2]. In this paper we show via simulation that this method may be inaccurate, making for instance 40% error in the simplest possible Gaussian case. We then propose a new estimation scheme which greatly improves the accuracy of the single-loop method, up to a factor 10 in some simple numerical examples. We prove that our estimator is strongly consistent and several simulation results are presented to demonstrate the advantages of the proposed method.

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  • Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2019. "A nonparametric importance sampling estimator for moment independent importance measures," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 3-16.
    • Handle: RePEc:eee:reensy:v:187:y:2019:i:c:p:3-16
    DOI: 10.1016/j.ress.2018.02.009
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    Cited by:

    1. Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2021. "Simultaneous estimation of complementary moment independent and reliability-oriented sensitivity measures," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 721-737.
    2. Liu, Gang & Gao, Kai & Yang, Qingshan & Tang, Wei & Law, S.S., 2021. "Improvement to the discretized initial condition of the generalized density evolution equation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Stefano Cucurachi & Carlos Felipe Blanco & Bernhard Steubing & Reinout Heijungs, 2022. "Implementation of uncertainty analysis and moment‐independent global sensitivity analysis for full‐scale life cycle assessment models," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 374-391, April.

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