Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Analysis of intensity for a queueing system: bootstrapping computation

Analysis of intensity for a queueing system: bootstrapping computation Traffic intensity plays an important role of system performance measures in queueing model. In this paper, we construct new confidence intervals of intensity for a queueing system, which are based on four bootstrap methods; standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval and bias-corrected and accelerated confidence interval. We also perform the accuracy of these bootstrap confidence intervals through calculating the coverage probability and the expected length of confidence intervals. Detailed discussions of the simulation results for four various types of queueing system are presented and some conclusions are provided. In addition, we demonstrate the four bootstrap confidence intervals with a real-life example. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

Analysis of intensity for a queueing system: bootstrapping computation

Loading next page...
 
/lp/inderscience-publishers/analysis-of-intensity-for-a-queueing-system-bootstrapping-computation-AK27yAjvrx
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2009.026949
Publisher site
See Article on Publisher Site

Abstract

Traffic intensity plays an important role of system performance measures in queueing model. In this paper, we construct new confidence intervals of intensity for a queueing system, which are based on four bootstrap methods; standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval and bias-corrected and accelerated confidence interval. We also perform the accuracy of these bootstrap confidence intervals through calculating the coverage probability and the expected length of confidence intervals. Detailed discussions of the simulation results for four various types of queueing system are presented and some conclusions are provided. In addition, we demonstrate the four bootstrap confidence intervals with a real-life example.

Journal

International Journal of Services Operations and InformaticsInderscience Publishers

Published: Jan 1, 2009

There are no references for this article.