TY - GEN
T1 - Quantile estimation using conditional Monte Carlo and Latin hypercube sampling
AU - Dong, Hui
AU - Nakayama, Marvin K.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85044537574&partnerID=8YFLogxK
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U2 - 10.1109/WSC.2017.8247933
DO - 10.1109/WSC.2017.8247933
M3 - Conference contribution
AN - SCOPUS:85044537574
T3 - Proceedings - Winter Simulation Conference
SP - 1986
EP - 1997
BT - 2017 Winter Simulation Conference, WSC 2017
A2 - Chan, Victor
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Winter Simulation Conference, WSC 2017
Y2 - 3 December 2017 through 6 December 2017
ER -