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

Romero-Silva, Rodrigo; Hurtado-Hernandez, Margarita

doi:10.1007/978-3-319-66963-2_25

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

Journal

Communications in Computer and Information Science

Applied Computer Sciences in Engineering

ISSN

1865-0929

1865-0937

Date Issued

2017

Author(s)

Type

Resource Types::text::book::book part

DOI

10.1007/978-3-319-66963-2_25

URL

https://scripta.up.edu.mx/handle/20.500.12552/4422

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. © 2017, Springer International Publishing AG.