Home // SIMUL 2016, The Eighth International Conference on Advances in System Simulation // View article
Authors:
Marvin Nakayama
Keywords: Monte Carlo; Variance Reduction; Risk Analysis; Value-at-Risk
Abstract:
We describe how to estimate a quantile when applying a combination of stratified sampling and conditional Monte Carlo, which are variance-reduction techniques for Monte Carlo simulations. We establish a central limit theorem for the resulting quantile estimator. We further prove that for any fixed stratification allocation, the asymptotic variance of the quantile estimator with a combination of stratified sampling and conditional Monte Carlo is no greater than that for stratified sampling alone. We explain how the methods may be used to efficiently perform a safety analysis of a nuclear power plant.
Pages: 6 to 10
Copyright: Copyright (c) IARIA, 2016
Publication date: August 21, 2016
Published in: conference
ISSN: 2308-4537
ISBN: 978-1-61208-501-2
Location: Rome, Italy
Dates: from August 21, 2016 to August 25, 2016