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Jana, Rittwik and Dey, Subhrakanti (2000) Change detection in teletraffic models. IEEE Transactions on Signal Processing, 48 (3). pp. 846-853. ISSN 1053-587X
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Abstract
In this paper, we propose a likelihood-based ratio
test to detect distributional changes in common teletraffic models.
These include traditional models like the Markov modulated
Poisson process and processes exhibiting long range dependency,
in particular, Gaussian fractional ARIMA processes. A practical
approach is also developed for the case where the parameter after
the change is unknown. It is noticed that the algorithm is robust
enough to detect slight perturbations of the parameter value after
the change. A comprehensive set of numerical results including
results for the mean detection delay is provided.
| Item Type: | Article |
|---|---|
| Keywords: | Autoregressive integrated moving average; change detection; long memory processes; Markov modulated Poisson process; |
| Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 14411 |
| Identification Number: | 10.1109/78.824678 |
| Depositing User: | Subhrakanti Dey |
| Date Deposited: | 10 May 2021 15:56 |
| Journal or Publication Title: | IEEE Transactions on Signal Processing |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/14411 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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