A novel approach to the estimation of the long-range dependence parameter

Author

Kettani, Houssain ; Gubner, John A.

Author_Institution

Dept. of Comput. Sci., Jackson State Univ., MS

Volume

53

Issue

6

fYear

2006

fDate

6/1/2006 12:00:00 AM

Firstpage

463

Lastpage

467

Abstract

A new method to estimate the Hurst parameter of certain classes of random processes is presented. This method applies to Gaussian processes that are either exactly second-order self-similar or fractional ARIMA. The case of the former is of special interest because local area network traffic is well-known to be of this form. Confidence intervals and bias are obtained for the estimates using the new method. The new method is then applied to pseudo-random data and to real traffic data. The performance of the new method is compared to that of the widely-used wavelet method, which demonstrates that the former is much faster and produces much smaller confidence intervals of the long-range dependence parameter

Keywords

Gaussian processes; local area networks; telecommunication traffic; wavelet transforms; Gaussian process; Hurst parameter; confidence intervals; fractional ARIMA; local area network traffic; long-range dependence parameter; pseudorandom data; random process; real traffic data; second-order self-similar; Analysis of variance; Gaussian processes; Local area networks; Parameter estimation; Performance analysis; Random processes; Statistical analysis; Telecommunication traffic; Wavelet analysis; Yield estimation; Estimation; long-range dependence; network traffic; self-similarity;

fLanguage

English

Journal_Title

Circuits and Systems II: Express Briefs, IEEE Transactions on

Publisher

ieee

ISSN

1549-7747

Type

jour

DOI

10.1109/TCSII.2006.873828

Filename

1643462