Estimating Sensitivity to Input Model Variance

Wendy Xi Jiang, Barry L. Nelson, L. Jeff Hong

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

Abstract

Simple question: How sensitive is your simulation output to the variance of your simulation input models? Unfortunately, the answer is not simple because the variance of many standard parametric input distributions can achieve the same change in multiple ways as a function of the parameters. In this paper we propose a family of output-mean-with-respect-to-input-variance sensitivity measures and identify two particularly useful members of it. A further benefit of this family is that there is a straightforward estimator of any member with no additional simulation effort beyond the nominal experiment. A numerical example is provided to illustrate the method and interpretation of results.

Original languageEnglish (US)
Title of host publication2019 Winter Simulation Conference, WSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3705-3716
Number of pages12
ISBN (Electronic)9781728132839
DOIs
StatePublished - Dec 2019
Event2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States
Duration: Dec 8 2019Dec 11 2019

Publication series

NameProceedings - Winter Simulation Conference
Volume2019-December
ISSN (Print)0891-7736

Conference

Conference2019 Winter Simulation Conference, WSC 2019
CountryUnited States
CityNational Harbor
Period12/8/1912/11/19

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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  • Cite this

    Jiang, W. X., Nelson, B. L., & Hong, L. J. (2019). Estimating Sensitivity to Input Model Variance. In 2019 Winter Simulation Conference, WSC 2019 (pp. 3705-3716). [9004684] (Proceedings - Winter Simulation Conference; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC40007.2019.9004684