Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

Yijie Peng; Michael C. Fu; Jiaqiao Hu; Pierre L'Ecuyer; Bruno Tuffin

doi:10.1109/WSC52266.2021.9715488

Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

Yijie Peng
, Michael C. Fu
, Jiaqiao Hu
, Pierre L'Ecuyer
, Bruno Tuffin

Applied Mathematics and Statistics

Peking University
University of Maryland, College Park
University of Montreal
Campus de Beaulieu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.

Original language	English
Title of host publication	2021 Winter Simulation Conference, WSC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665433112
DOIs	https://doi.org/10.1109/WSC52266.2021.9715488
State	Published - 2021
Event	2021 Winter Simulation Conference, WSC 2021 - Phoenix, United States Duration: Dec 12 2021 → Dec 15 2021

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2021-December
ISSN (Print)	0891-7736

Conference

Conference	2021 Winter Simulation Conference, WSC 2021
Country/Territory	United States
City	Phoenix
Period	12/12/21 → 12/15/21

Access to Document

10.1109/WSC52266.2021.9715488

Cite this

Peng, Y., Fu, M. C., Hu, J., L'Ecuyer, P., & Tuffin, B. (2021). Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation. In 2021 Winter Simulation Conference, WSC 2021 (Proceedings - Winter Simulation Conference; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC52266.2021.9715488

@inproceedings{446ef28dcedf419094765bf478436907,

title = "Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation",

abstract = "We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.",

author = "Yijie Peng and Fu, \{Michael C.\} and Jiaqiao Hu and Pierre L'Ecuyer and Bruno Tuffin",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 Winter Simulation Conference, WSC 2021 ; Conference date: 12-12-2021 Through 15-12-2021",

year = "2021",

doi = "10.1109/WSC52266.2021.9715488",

language = "English",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 Winter Simulation Conference, WSC 2021",

}

Peng, Y, Fu, MC, Hu, J, L'Ecuyer, P & Tuffin, B 2021, Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation. in 2021 Winter Simulation Conference, WSC 2021. Proceedings - Winter Simulation Conference, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., 2021 Winter Simulation Conference, WSC 2021, Phoenix, United States, 12/12/21. https://doi.org/10.1109/WSC52266.2021.9715488

Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation. / Peng, Yijie; Fu, Michael C.; Hu, Jiaqiao et al.
2021 Winter Simulation Conference, WSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings - Winter Simulation Conference; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

AU - Peng, Yijie

AU - Fu, Michael C.

AU - Hu, Jiaqiao

AU - L'Ecuyer, Pierre

AU - Tuffin, Bruno

PY - 2021

Y1 - 2021

N2 - We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.

AB - We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant.

UR - https://www.scopus.com/pages/publications/85126113177

U2 - 10.1109/WSC52266.2021.9715488

DO - 10.1109/WSC52266.2021.9715488

M3 - Conference contribution

AN - SCOPUS:85126113177

T3 - Proceedings - Winter Simulation Conference

BT - 2021 Winter Simulation Conference, WSC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 Winter Simulation Conference, WSC 2021

Y2 - 12 December 2021 through 15 December 2021

ER -

Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

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