Efficient quantile estimation via a combination of importance sampling and Latin hypercube sampling
Document Type
Conference Proceeding
Publication Date
1-1-2017
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.
Identifier
85050027539 (Scopus)
ISBN
[9789492859006]
Publication Title
31st Annual European Simulation and Modelling Conference 2017 Esm 2017
First Page
49
Last Page
53
Grant
CMMI-1537322
Fund Ref
National Science Foundation
Recommended Citation
Nakayama, Marvin K., "Efficient quantile estimation via a combination of importance sampling and Latin hypercube sampling" (2017). Faculty Publications. 9943.
https://digitalcommons.njit.edu/fac_pubs/9943
