Quantile estimation using conditional Monte Carlo and Latin hypercube sampling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Quantiles are often employed to measure risk. We combine two variance-reduction techniques, conditional Monte Carlo and Latin hypercube sampling, to estimate a quantile. Compared to either method by itself, the combination can produce a quantile estimator with substantially smaller variance. In addition to devising a point estimator for the quantile when applying the combined approaches, we also describe how to construct confidence intervals for the quantile. Numerical results demonstrate the effectiveness of the methods.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1986-1997
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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