with long-range dependence should have a periodogram, which is proportional to close to the origin. The periodogram plot is obtained by plotting against . Then fitting a regression model of the on the should give a slope of . The slope of the fitted straight line is also the estimation of . Thus, the estimation is obtained. Note: There are two common problems when we apply the periodogram method. First, if the data does not follow a Gaussian distribution, transformation of the data can solve this kind of problems. Second, the sample spectrum which deviates from the assumed spectral density is another one. An aggregation method is suggested to solve this problem. If is a Gaussian process and the spectral density function of satisfies as , the function, , converges in distribution to FGN as . References P. Whittle, "Estimation and information in stationary time series", Art. Mat. 2, 423-434, 1953. K. PARK, W. WILLINGER, Self-Similar Network Traffic and Performance Evaluation, WILEY,2000. W. E. Leland, W. Willinger, M. S. Taqqu, D. V. Wilson, "On the self-similar nature of Ethernet traffic", ACM SIGCOMM Computer Communication Review 25,202-213,1995. W. Willinger, M. S. Taqqu, W. E. Leland, D. V. Wilson, "Self-Similarity in High-Speed Packet Traffic: Analysis and Modeling of Ethernet Traffic Measurements", Statistical Science 10,67-85,1995. Computer network analysis