Quantile estimation when applying conditional Monte Carlo

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

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSIMULTECH 2014 - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
EditorsMohammad S. Obaidat, Janusz Kacprzyk, Tuncer Oren
PublisherSciTePress
ISBN (Electronic)9789897580383
StatePublished - 2014
Event4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2014 - Vienna, Austria
Duration: Aug 28 2014Aug 30 2014

Publication series

NameSIMULTECH 2014 - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Conference

Conference4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2014
Country/TerritoryAustria
CityVienna
Period8/28/148/30/14

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Conditional Monte Carlo
  • Confidence interval
  • Quantile
  • Value-at-risk
  • Variance reduction

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