@inproceedings{eef2da3daab8417dbc900b5f4b47881c,
title = "Efficient quantile estimation via a combination of importance sampling and Latin hypercube sampling",
abstract = "Many application areas employ a quantile, also known as a percentile or value-at-risk, to measure risk of a stochastic system. We present efficient Monte Carlo methods to estimate a quantile through a combination of importance sampling and Latin hypercube sampling. We also give numerical results from a simple model showing that the combined methods can outperform each by itself.",
keywords = "Risk analysis, Value-at-risk, Variance reduction",
author = "Nakayama, {Marvin K.}",
note = "Publisher Copyright: {\textcopyright} 2017 EUROSIS-ETI. All rights reserved.; 31st Annual European Simulation and Modelling Conference, ESM 2017 ; Conference date: 25-10-2017 Through 27-10-2017",
year = "2017",
language = "English (US)",
series = "31st Annual European Simulation and Modelling Conference 2017, ESM 2017",
publisher = "EUROSIS",
pages = "49--53",
editor = "Goncalves, {Paulo J.S.}",
booktitle = "31st Annual European Simulation and Modelling Conference 2017, ESM 2017",
}