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

Marvin K. Nakayama, Zachary T. Kaplan, Yajuan Li, Bruno Tuffin, Pierre L'Ecuyer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. To get 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).

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Winter Simulation Conference, WSC 2020
EditorsK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages301-312
Number of pages12
ISBN (Electronic)9781728194998
DOIs
StatePublished - Dec 14 2020
Event2020 Winter Simulation Conference, WSC 2020 - Orlando, United States
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings - Winter Simulation Conference
Volume2020-December
ISSN (Print)0891-7736

Conference

Conference2020 Winter Simulation Conference, WSC 2020
CountryUnited States
CityOrlando
Period12/14/2012/18/20

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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