Queue response to input correlation functions: discrete spectral analysis
IEEE/ACM Transactions on Networking (TON)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
On the use of self-similar processes in network simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and simulation of communication networks
A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation
Performance Evaluation
Queueing at large resources driven by long-tailed M/G/\infty-modulated processes
Queueing Systems: Theory and Applications
The M/G/∞ system revisited: finiteness, summability, long range dependence, and reverse engineering
Queueing Systems: Theory and Applications
Flexible adjustment of the short-term correlation of LRD M/G/∞-based processes
Electronic Notes in Theoretical Computer Science (ENTCS)
Fast simulation of self-similar and correlated processes
Mathematics and Computers in Simulation
Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
In this paper, we analyze the distribution of the statistic of a test for identifying the type of correlated time series. The rule for selecting a model suitable to the data is based on the comparison between the normalized prediction errors of the Whittle estimator applied to the candidate models. We consider one application of the test: assessing the significance of increasing the number of parameters within a given class of models. The results obtained demonstrate that the Weibull distribution is a good approximation for the distribution of the test statistic.