Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo
Document Type
Conference Proceeding
Publication Date
12-14-2020
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).
Identifier
85103885569 (Scopus)
ISBN
[9781728194998]
Publication Title
Proceedings Winter Simulation Conference
External Full Text Location
https://doi.org/10.1109/WSC48552.2020.9384031
ISSN
08917736
First Page
301
Last Page
312
Volume
2020-December
Grant
CMMI-1537322
Fund Ref
National Science Foundation
Recommended Citation
Nakayama, Marvin K.; Kaplan, Zachary T.; Li, Yajuan; Tuffin, Bruno; and L'Ecuyer, Pierre, "Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo" (2020). Faculty Publications. 4742.
https://digitalcommons.njit.edu/fac_pubs/4742
