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:	https://doi.org/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
URI:	Publisher
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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