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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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Dates and versions

hal-02551516 , version 1 (23-04-2020)

Identifiers

  • HAL Id : hal-02551516 , version 1

Cite

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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