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Estimation of Quantile Confidence Intervals for Queueing Systems Based on the Bootstrap Methodology

  • Rodrigo Romero-SilvaEmail author
  • Margarita Hurtado
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 742)

Abstract

This paper presents a simple methodology for estimating confidence intervals of quantiles in queueing systems. The paper investigates the actual probability density function of quantile estimators resulting of independent replications. Furthermore, we present a methodology, based on the concepts of bootstrapping, i.e., re-sampling and sub-sampling, to calculate the variability of an estimator without running different independent replications. Contrary to what overlapping and non-overlapping batching procedures suggest, we propose to randomly select data points to form a sub-sample, instead of selecting time-consecutive data points. The results of this study suggest that this proposal reduces the correlation between sub-samples (or batches) and overcomes the issue of normality.

Keywords

Bootstrapping Non-overlapping batches Confidence intervals Discrete-Event Simulation Quantiles 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversidad PanamericanaMexico CityMexico

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