@inproceedings{ecf78cad2e9c4148b0a3866328c0d377,
title = "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. 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).",
author = "Nakayama, \{Marvin K.\} and Kaplan, \{Zachary T.\} and Yajuan Li and Bruno Tuffin and Pierre L'Ecuyer",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Winter Simulation Conference, WSC 2020 ; Conference date: 14-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/WSC48552.2020.9384031",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "301--312",
editor = "K.-H. Bae and B. Feng and S. Kim and S. Lazarova-Molnar and Z. Zheng and T. Roeder and R. Thiesing",
booktitle = "Proceedings of the 2020 Winter Simulation Conference, WSC 2020",
address = "United States",
}