Better input modeling via model averaging

Wendy Xi Jiang, Barry L Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Rather than the standard practice of selecting a single “best-fit” distribution from a candidate set, frequentist model averaging (FMA) forms a mixture distribution that is a weighted average of the candidate distributions with the weights tuned by cross-validation. In previous work we showed theoretically and empirically that FMA in the probability space leads to higher fidelity input distributions. In this paper we show that FMA can also be implemented in the quantile space, leading to fits that emphasize tail behavior. We also describe an R package for FMA that is easy to use and available for download.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1575-1586
Number of pages12
ISBN (Electronic)9781538665725
DOIs
StatePublished - Jan 31 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
Volume2018-December
ISSN (Print)0891-7736

Conference

Conference2018 Winter Simulation Conference, WSC 2018
CountrySweden
CityGothenburg
Period12/9/1812/12/18

Fingerprint

Model Averaging
Modeling
Mixture Distribution
Tail Behavior
Probability Space
Weighted Average
Quantile
Cross-validation
Fidelity

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Jiang, W. X., & Nelson, B. L. (2019). Better input modeling via model averaging. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause (pp. 1575-1586). [8632239] (Proceedings - Winter Simulation Conference; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2018.8632239
Jiang, Wendy Xi ; Nelson, Barry L. / Better input modeling via model averaging. WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1575-1586 (Proceedings - Winter Simulation Conference).
@inproceedings{e28fb28664e44ab48e3f310f9226252e,
title = "Better input modeling via model averaging",
abstract = "Rather than the standard practice of selecting a single “best-fit” distribution from a candidate set, frequentist model averaging (FMA) forms a mixture distribution that is a weighted average of the candidate distributions with the weights tuned by cross-validation. In previous work we showed theoretically and empirically that FMA in the probability space leads to higher fidelity input distributions. In this paper we show that FMA can also be implemented in the quantile space, leading to fits that emphasize tail behavior. We also describe an R package for FMA that is easy to use and available for download.",
author = "Jiang, {Wendy Xi} and Nelson, {Barry L}",
year = "2019",
month = "1",
day = "31",
doi = "10.1109/WSC.2018.8632239",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1575--1586",
booktitle = "WSC 2018 - 2018 Winter Simulation Conference",
address = "United States",

}

Jiang, WX & Nelson, BL 2019, Better input modeling via model averaging. in WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause., 8632239, Proceedings - Winter Simulation Conference, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 1575-1586, 2018 Winter Simulation Conference, WSC 2018, Gothenburg, Sweden, 12/9/18. https://doi.org/10.1109/WSC.2018.8632239

Better input modeling via model averaging. / Jiang, Wendy Xi; Nelson, Barry L.

WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1575-1586 8632239 (Proceedings - Winter Simulation Conference; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Better input modeling via model averaging

AU - Jiang, Wendy Xi

AU - Nelson, Barry L

PY - 2019/1/31

Y1 - 2019/1/31

N2 - Rather than the standard practice of selecting a single “best-fit” distribution from a candidate set, frequentist model averaging (FMA) forms a mixture distribution that is a weighted average of the candidate distributions with the weights tuned by cross-validation. In previous work we showed theoretically and empirically that FMA in the probability space leads to higher fidelity input distributions. In this paper we show that FMA can also be implemented in the quantile space, leading to fits that emphasize tail behavior. We also describe an R package for FMA that is easy to use and available for download.

AB - Rather than the standard practice of selecting a single “best-fit” distribution from a candidate set, frequentist model averaging (FMA) forms a mixture distribution that is a weighted average of the candidate distributions with the weights tuned by cross-validation. In previous work we showed theoretically and empirically that FMA in the probability space leads to higher fidelity input distributions. In this paper we show that FMA can also be implemented in the quantile space, leading to fits that emphasize tail behavior. We also describe an R package for FMA that is easy to use and available for download.

UR - http://www.scopus.com/inward/record.url?scp=85062620995&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062620995&partnerID=8YFLogxK

U2 - 10.1109/WSC.2018.8632239

DO - 10.1109/WSC.2018.8632239

M3 - Conference contribution

T3 - Proceedings - Winter Simulation Conference

SP - 1575

EP - 1586

BT - WSC 2018 - 2018 Winter Simulation Conference

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Jiang WX, Nelson BL. Better input modeling via model averaging. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1575-1586. 8632239. (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2018.8632239