A nonparametric importance sampling estimator for moment independent importance measures

Pierre Derennes, Morio, Jérôme and Florian Simatos

Reliability Engineering and System Safety, 2019, vol. 187, issue C, 3-16

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.

Keywords: Monte Carlo simulation; Importance sampling; Density-based sensitivity analysis; Importance measures (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:187:y:2019:i:c:p:3-16

DOI: 10.1016/j.ress.2018.02.009

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