Estimation of quantile confidence intervals for queueing systems based on the bootstrap methodology

Rodrigo Romero-Silva*, Margarita Hurtado

*Corresponding author for this work

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


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.

Original languageEnglish
Title of host publicationApplied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings
EditorsJuan Carlos Figueroa-Garcia, Eduyn Ramiro Lopez-Santana, Roberto Ferro-Escobar, Jose Luis Villa-Ramirez
Number of pages12
ISBN (Print)9783319669625
Publication statusPublished - 2017
Externally publishedYes
Event4th Workshop on Engineering Applications, WEA 2017 - Cartagena, Colombia
Duration: 27 Sep 201729 Sep 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference4th Workshop on Engineering Applications, WEA 2017


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


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