Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

7-2011

Journal or Book Title

Journal of Applied Statistics

Volume

38

Issue

7

First Page

1407

Last Page

1433

DOI

10.1080/02664763.2010.505949

Abstract

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.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis as Park, Cheolwoo, Félix Hernández-Campos, Long Le, J. S. Marron, Juhyun Park, Vladas Pipiras, F. D. Smith, Richard L. Smith, Michele Trovero, and Zhengyuan Zhu. "Long-range dependence analysis of Internet traffic." Journal of Applied Statistics 38, no. 7 (2011): 1407-1433. Available online DOI: 10.1080/02664763.2010.505949. Posted with permission.

Copyright Owner

Taylor & Francis

Language

en

File Format

application/pdf

Published Version

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