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Efficient budget allocation strategies for elementary effects method in stochastic simulation

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  • Wen Shi
  • Xi Chen

Abstract

This paper focuses on extending the Morris' elementary effects method (MM) for sensitivity analysis/factor screening originated in the context of deterministic computer experiments to the stochastic simulation setting. Given a fixed simulation budget to expend, the main objective is to provide efficient and accurate estimates of main and interaction (or nonlinear) effects coined by the standard MM for characterizing the importance of each factor, despite the impact of simulation errors. Taking into account both the factor/input sampling uncertainty rooted in MM and the random errors inherent in a stochastic simulation, we develop efficient budget allocation strategies for implementing MM in this new context. Under each strategy proposed, we derive its corresponding optimal budget partition and optimal budget allocation rules. Numerical results corroborate the practical effectiveness of the proposed budget allocation strategies.

Suggested Citation

  • Wen Shi & Xi Chen, 2018. "Efficient budget allocation strategies for elementary effects method in stochastic simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(3), pages 218-241, April.
    • Handle: RePEc:wly:navres:v:65:y:2018:i:3:p:218-241
    DOI: 10.1002/nav.21802
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    References listed on IDEAS

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    2. Qiyun Pan & Eunshin Byon & Young Myoung Ko & Henry Lam, 2020. "Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(7), pages 524-547, October.

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