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Conference Papers Year : 2021

Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

Abstract

We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.
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Dates and versions

hal-03196364 , version 1 (12-04-2021)

Identifiers

  • HAL Id : hal-03196364 , version 1

Cite

Yijie Peng, Michael C Fu, Jiaqiao Hu, Pierre L'Ecuyer, Bruno Tuffin. Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation. 2021 - Winter Simulation Conference, Dec 2021, Phoenix, United States. pp.1-12. ⟨hal-03196364⟩
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