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Quantile estimation when applying conditional Monte Carlo | IEEE Conference Publication | IEEE Xplore

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 s...Show More

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.
Date of Conference: 28-30 August 2014
Date Added to IEEE Xplore: 27 April 2015
Electronic ISBN:978-989-758-060-4
Conference Location: Vienna, Austria

References

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