Quantile Estimation When Applying Conditional Monte Carlo

Marvin K. Nakayama

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.

References

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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