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Input Uncertainty Quantification for Quantiles

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Input Uncertainty Quantification for Quantiles. / Parmar, Drupad; Morgan, Lucy; Titman, Andrew et al.
2022 Winter Simulation Conference (WSC). ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. IEEE, 2023. p. 97-108.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Parmar, D, Morgan, L, Titman, A, Sanchez, S & Williams, R 2023, Input Uncertainty Quantification for Quantiles. in B Feng, G Pedrielli, Y Peng, S Shashaani, E Song, CG Corlu, LH Lee, EP Chew, T Roeder & P Lendermann (eds), 2022 Winter Simulation Conference (WSC). IEEE, pp. 97-108. https://doi.org/10.1109/WSC57314.2022.10015272

APA

Parmar, D., Morgan, L., Titman, A., Sanchez, S., & Williams, R. (2023). Input Uncertainty Quantification for Quantiles. In B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C. G. Corlu, L. H. Lee, E. P. Chew, T. Roeder, & P. Lendermann (Eds.), 2022 Winter Simulation Conference (WSC) (pp. 97-108). IEEE. https://doi.org/10.1109/WSC57314.2022.10015272

Vancouver

Parmar D, Morgan L, Titman A, Sanchez S, Williams R. Input Uncertainty Quantification for Quantiles. In Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Chew EP, Roeder T, Lendermann P, editors, 2022 Winter Simulation Conference (WSC). IEEE. 2023. p. 97-108 Epub 2022 Dec 11. doi: 10.1109/WSC57314.2022.10015272

Author

Parmar, Drupad ; Morgan, Lucy ; Titman, Andrew et al. / Input Uncertainty Quantification for Quantiles. 2022 Winter Simulation Conference (WSC). editor / B. Feng ; G. Pedrielli ; Y. Peng ; S. Shashaani ; E. Song ; C.G. Corlu ; L.H. Lee ; E.P. Chew ; T. Roeder ; P. Lendermann. IEEE, 2023. pp. 97-108

Bibtex

@inproceedings{59c118567905432ca5aaab4dba250c9a,
title = "Input Uncertainty Quantification for Quantiles",
abstract = "Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analyticalapproximation of the true value of input uncertainty.",
author = "Drupad Parmar and Lucy Morgan and Andrew Titman and Susan Sanchez and Richard Williams",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2023",
month = jan,
day = "23",
doi = "10.1109/WSC57314.2022.10015272",
language = "English",
isbn = "9781665476621",
pages = "97--108",
editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",
booktitle = "2022 Winter Simulation Conference (WSC)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Input Uncertainty Quantification for Quantiles

AU - Parmar, Drupad

AU - Morgan, Lucy

AU - Titman, Andrew

AU - Sanchez, Susan

AU - Williams, Richard

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2023/1/23

Y1 - 2023/1/23

N2 - Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analyticalapproximation of the true value of input uncertainty.

AB - Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analyticalapproximation of the true value of input uncertainty.

U2 - 10.1109/WSC57314.2022.10015272

DO - 10.1109/WSC57314.2022.10015272

M3 - Conference contribution/Paper

SN - 9781665476621

SP - 97

EP - 108

BT - 2022 Winter Simulation Conference (WSC)

A2 - Feng, B.

A2 - Pedrielli, G.

A2 - Peng, Y.

A2 - Shashaani, S.

A2 - Song, E.

A2 - Corlu, C.G.

A2 - Lee, L.H.

A2 - Chew, E.P.

A2 - Roeder, T.

A2 - Lendermann, P.

PB - IEEE

ER -