Adaptive Minimal Confidence Region Rule for Multivariate Initialization Bias Truncation in Discrete-Event Simulations

Jianguo Wu, Honglun Xu, Feng Ju, Tzu Liang Tseng

Research output: Contribution to journalArticle

Abstract

Initialization bias truncation is critically important for system performance assessment and warm-up length estimation in discrete-event simulations. Most of the existing methods are for univariate signals, while multivariate truncation has been rarely studied. To fill such gap, this article proposes an efficient method, called adaptive minimal confidence region rule (AMCR) for multivariate initialization bias truncation. It determines the truncation point by minimizing the modified confidence volume with a tuning parameter for the mean estimate. An elbow method is developed for adaptive selection of the tuning parameter. Theoretical properties of the AMCR rule for both data with and without autocorrelations have been derived for justification and practical guidance. The effectiveness and superiority of the AMCR rule over other existing approaches have been demonstrated through thorough numerical studies and real application. Supplementary materials for this article are available online.

Original languageEnglish (US)
JournalTechnometrics
DOIs
StateAccepted/In press - Jan 1 2019

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Keywords

  • Asymptotically unbiased estimator
  • Autocorrelation
  • Generalized variance
  • Minimal confidence region
  • Steady state

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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