Quantile Estimation When Applying Conditional Monte Carlo

Marvin K. Nakayama

2014

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.

Paper Citation

in Harvard Style

K. Nakayama M. (2014). Quantile Estimation When Applying Conditional Monte Carlo . In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-038-3, pages 280-285. DOI: 10.5220/0005109702800285

in Bibtex Style

@conference{simultech14,
author={Marvin K. Nakayama},
title={Quantile Estimation When Applying Conditional Monte Carlo},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2014},
pages={280-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005109702800285},
isbn={978-989-758-038-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Quantile Estimation When Applying Conditional Monte Carlo
SN - 978-989-758-038-3
AU - K. Nakayama M.
PY - 2014
SP - 280
EP - 285
DO - 10.5220/0005109702800285