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

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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  • 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