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Long-range dependence analysis of Internet traffic

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Long-range dependence analysis of Internet traffic. / Park, Cheolwoo; Hernandez-Campos, Felix; Le, Long; Marron, J. S.; Park, Juhyun; Pipiras, Vladas; Smith, F. D.; Smith, Richard L.; Trovero, Michele; Zhu, Zhengyuan.

In: Journal of Applied Statistics, Vol. 38, No. 7, 2011, p. 1407-1433.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Park, C, Hernandez-Campos, F, Le, L, Marron, JS, Park, J, Pipiras, V, Smith, FD, Smith, RL, Trovero, M & Zhu, Z 2011, 'Long-range dependence analysis of Internet traffic', Journal of Applied Statistics, vol. 38, no. 7, pp. 1407-1433. https://doi.org/10.1080/02664763.2010.505949

APA

Park, C., Hernandez-Campos, F., Le, L., Marron, J. S., Park, J., Pipiras, V., Smith, F. D., Smith, R. L., Trovero, M., & Zhu, Z. (2011). Long-range dependence analysis of Internet traffic. Journal of Applied Statistics, 38(7), 1407-1433. https://doi.org/10.1080/02664763.2010.505949

Vancouver

Park C, Hernandez-Campos F, Le L, Marron JS, Park J, Pipiras V et al. Long-range dependence analysis of Internet traffic. Journal of Applied Statistics. 2011;38(7):1407-1433. https://doi.org/10.1080/02664763.2010.505949

Author

Park, Cheolwoo ; Hernandez-Campos, Felix ; Le, Long ; Marron, J. S. ; Park, Juhyun ; Pipiras, Vladas ; Smith, F. D. ; Smith, Richard L. ; Trovero, Michele ; Zhu, Zhengyuan. / Long-range dependence analysis of Internet traffic. In: Journal of Applied Statistics. 2011 ; Vol. 38, No. 7. pp. 1407-1433.

Bibtex

@article{a6f7c298372a42a6b18b195371a5fe8a,
title = "Long-range dependence analysis of Internet traffic",
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.",
keywords = "Hurst parameter, Internet traffic , long-range dependence, multiscale analysis , non-stationarity",
author = "Cheolwoo Park and Felix Hernandez-Campos and Long Le and Marron, {J. S.} and Juhyun Park and Vladas Pipiras and Smith, {F. D.} and Smith, {Richard L.} and Michele Trovero and Zhengyuan Zhu",
year = "2011",
doi = "10.1080/02664763.2010.505949",
language = "English",
volume = "38",
pages = "1407--1433",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "7",

}

RIS

TY - JOUR

T1 - Long-range dependence analysis of Internet traffic

AU - Park, Cheolwoo

AU - Hernandez-Campos, Felix

AU - Le, Long

AU - Marron, J. S.

AU - Park, Juhyun

AU - Pipiras, Vladas

AU - Smith, F. D.

AU - Smith, Richard L.

AU - Trovero, Michele

AU - Zhu, Zhengyuan

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Hurst parameter

KW - Internet traffic

KW - long-range dependence

KW - multiscale analysis

KW - non-stationarity

U2 - 10.1080/02664763.2010.505949

DO - 10.1080/02664763.2010.505949

M3 - Journal article

VL - 38

SP - 1407

EP - 1433

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 7

ER -