Park, Cheolwoo and Hernandez-Campos, Felix and Le, Long and Marron, J. S. and Park, Juhyun and Pipiras, Vladas and Smith, F. D. and Smith, Richard L. and Trovero, Michele and Zhu, Zhengyuan (2011) Long-range dependence analysis of Internet traffic. Journal of Applied Statistics, 38 (7). pp. 1407-1433. ISSN 0266-4763Full text not available from this repository.
Long-range-dependent time series are endemic in the statistical analysis of Internet traffic. The Hurst parameter provides a good summary of important self-similar scaling properties. We compare a number of different Hurst parameter estimation methods and some important variations. This is done in the context of a wide range of simulated, laboratory-generated, and real data sets. Important differences between the methods are highlighted. Deep insights are revealed on how well the laboratory data mimic the real data. Non-stationarities, which are local in time, are seen to be central issues and lead to both conceptual and practical recommendations.
|Journal or Publication Title:||Journal of Applied Statistics|
|Uncontrolled Keywords:||Hurst parameter ; Internet traffic ; long-range dependence ; multiscale analysis ; non-stationarity|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited On:||28 May 2012 11:38|
|Last Modified:||03 Nov 2015 15:53|
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