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Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo

Abstract : We consider the problem of estimating the p-quantile of a distribution when observations from that distribution are generated from a simulation model. The standard estimator takes the p-quantile of the empirical distribution of independent observations obtained by Monte Carlo. As an improvement, we use conditional Monte Carlo to obtain a smoother estimate of the distribution function, and we combine this with randomized quasi-Monte Carlo to further reduce the variance. The result is a much more accurate quantile estimator, whose mean square error can converge even faster than the canonical rate of O(1/n).
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https://hal.inria.fr/hal-02551516
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Submitted on : Thursday, April 23, 2020 - 6:25:54 AM
Last modification on : Monday, April 4, 2022 - 9:28:24 AM

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  • HAL Id : hal-02551516, version 1

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Marvin K Nakayama, Zachary T Kaplan, Bruno Tuffin, Pierre L'Ecuyer. Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo. Winter Simulation Conference 2020, Dec 2020, Orlando, United States. ⟨hal-02551516⟩

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