分位数敏感性估计中广义似然比方法的方差减小

2021 Winter Simulation Conference (WSC) Pub Date : 2021-12-12 DOI:10.1109/WSC52266.2021.9715488

Yijie Peng, M. Fu, Jiaqiao Hu, P. L'Ecuyer, B. Tuffin

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引用次数: 3

摘要

我们在Peng et al.(2018)和Peng et al.(2021)中应用广义似然比(GLR)方法来估计分位数敏感性。采用条件蒙特卡罗和随机拟蒙特卡罗方法减小GLR估计量的方差。将所提出的方法应用于一个玩具实例和一个随机活动网络实例。数值结果表明，该方法的方差减小效果显著。

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Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

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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2021 Winter Simulation Conference (WSC)

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