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Conference papers

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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Conference papers
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https://hal.inria.fr/hal-03196364
Contributor : Bruno Tuffin Connect in order to contact the contributor
Submitted on : Monday, April 12, 2021 - 5:33:01 PM
Last modification on : Monday, April 4, 2022 - 9:28:23 AM
Long-term archiving on: : Tuesday, July 13, 2021 - 7:10:51 PM

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  • HAL Id : hal-03196364, version 1

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Yijie Peng, Michael 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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