An empirical estimation of statistical inferences for system dynamics model parameters

Mohammed Mesabbah, Wael Rashwan, Amr Arisha

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

For system dynamics simulation (SD) models, an estimation of statistical distributions for uncertain parameters is crucial. These distributions could be used for testing models sensitivity, quality of policies, and/or estimating confidence intervals for these parameters. Assumptions related to normality, independence and constant variation are often misapplied in dynamic simulation. Bootstrapping holds a considerable theoretical advantage when used with non-Gaussian data for estimating empirical distributions for unknown parameters. Although it is a widely acceptable approach, it has had only limited use in system dynamics applications. This paper introduces an application of Direct Residual Bootstrapping (DRBS) for statistical inference in system dynamic model. DRBS has been applied successfully to 'The Irish Elderly Patient Delayed Discharge' dynamic model to estimate empirical distribution for some unknown parameters with a minimal computation effort. The computational results show that bootstrapping offers an efficient performance in cases of no availability of prior information of model parameters.

Original languageEnglish
Title of host publicationProceedings of the 2014 Winter Simulation Conference, WSC 2014
EditorsAndreas Tolk, Levent Yilmaz, Saikou Y. Diallo, Ilya O. Ryzhov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-697
Number of pages12
ISBN (Electronic)9781479974863
DOIs
Publication statusPublished - 23 Jan 2015
Event2014 Winter Simulation Conference, WSC 2014 - Savannah, United States
Duration: 7 Dec 201410 Dec 2014

Publication series

NameProceedings - Winter Simulation Conference
Volume2015-January
ISSN (Print)0891-7736

Conference

Conference2014 Winter Simulation Conference, WSC 2014
Country/TerritoryUnited States
CitySavannah
Period7/12/1410/12/14

Keywords

  • system dynamics simulation
  • statistical distributions
  • uncertain parameters
  • model sensitivity
  • quality of policies
  • confidence intervals
  • normality
  • independence
  • constant variation
  • dynamic simulation
  • bootstrapping
  • non-Gaussian data
  • empirical distributions
  • Direct Residual Bootstrapping
  • statistical inference
  • Irish Elderly Patient Delayed Discharge
  • computation effort
  • computational results
  • prior information

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