@inproceedings{55f667791b9d40ae89e409102a0d3cee,
title = "Quantile estimation when applying conditional Monte Carlo",
abstract = "We describe how to use conditional Monte Carlo (CMC) to estimate a quantile. CMC is a variance-reduction technique that reduces variance by analytically integrating out some of the variability. We show that the CMC quantile estimator satisfies a central limit theorem and Bahadur representation. We also develop three asymptotically valid confidence intervals (CIs) for a quantile. One CI is based on a finite-difference estimator, another uses batching, and the third applies sectioning. We present numerical results demonstrating the effectiveness of CMC.",
keywords = "Conditional Monte Carlo, Confidence interval, Quantile, Value-at-risk, Variance reduction",
author = "Nakayama, {Marvin K.}",
note = "Publisher Copyright: {\textcopyright} 2014 SciTePress. All rights reserved.; 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2014 ; Conference date: 28-08-2014 Through 30-08-2014",
year = "2014",
language = "English (US)",
series = "SIMULTECH 2014 - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications",
publisher = "SciTePress",
editor = "Obaidat, {Mohammad S.} and Janusz Kacprzyk and Tuncer Oren",
booktitle = "SIMULTECH 2014 - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications",
}