## Semi-Parametric Graphical Estimation Techniques for Long-Memory Data. (1996)

Citations: | 15 - 4 self |

### BibTeX

@MISC{Taqqu96semi-parametricgraphical,

author = {Murad S. Taqqu and Vadim Teverovsky},

title = {Semi-Parametric Graphical Estimation Techniques for Long-Memory Data.},

year = {1996}

}

### Years of Citing Articles

### OpenURL

### Abstract

This paper reviews several periodogram-based methods for estimating the long-memory parameter H in time series and suggests a way to robustify them. The high frequencies tend to bias the estimates. Using only low frequencies eliminates the bias but increases the variance. We hence suggest plotting the estimates of H as a function of a parameter which balances bias versus variance and, if the plot flattens in a central region, to use the flat part for estimating H. We apply this technique to the periodogram regression method, the Whittle approximation to maximum likelihood and to the local Whittle method. We investigate its effectiveness on several simulated fractional ARIMA series and also apply it to estimate the long-memory parameter H in computer network traffic. 1 Introduction Time series with long memory have been considered in many fields including hydrology, biology and computer networks. Unfortunately, estimating the long memory (long-range dependence) parameter H in a given d...