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Titolo:
A wavelet-based joint estimator of the parameters of long-range dependence
Autore:
Veitch, D; Abry, P;
Indirizzi:
Software Engn Res Ctr, Carlton, Vic 3053, Australia Software Engn Res CtrCarlton Vic Australia 3053 ton, Vic 3053, Australia Ecole Normale Super Lyon, Phys Lab, CNRS, URA 1325, F-69364 Lyon 07, France Ecole Normale Super Lyon Lyon France 07 RA 1325, F-69364 Lyon 07, France
Titolo Testata:
IEEE TRANSACTIONS ON INFORMATION THEORY
fascicolo: 3, volume: 45, anno: 1999,
pagine: 878 - 897
SICI:
0018-9448(199904)45:3<878:AWJEOT>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
FRACTIONAL BROWNIAN-MOTION; STORAGE MODEL;
Keywords:
Hurst parameter; long-range dependence; packet traffic; parameter estimation; telecommunications networks; time-scale analysis; wavelet decomposition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
30
Recensione:
Indirizzi per estratti:
Indirizzo: Veitch, D Software Engn Res Ctr, Carlton, Vic 3053, Australia Software Engn Res Ctr Carlton Vic Australia 3053 053, Australia
Citazione:
D. Veitch e P. Abry, "A wavelet-based joint estimator of the parameters of long-range dependence", IEEE INFO T, 45(3), 1999, pp. 878-897

Abstract

A joint estimator is presented for the two parameters that define the long-range dependence phenomenon in the simplest case. The estimator is based on the coefficients of a discrete wavelet decomposition, improving a recently proposed wavelet-based estimator of the scaling parameter [4], as well asextending it to include the associated power parameter. An important feature is its conceptual and practical simplicity, consisting essentially in measuring the slope and the intercept of a linear fit after a discrete wavelet transform is performed, a very fast (O(n)) operation. Under well-justified technical idealizations the estimator is shown to be unbiased and of minimum or close to minimum variance for the scale parameter, and asymptotically unbiased and efficient for the second parameter. Through theoretical arguments and numerical simulations it is shown that in practice, even for small data sets, the bias is very small and the variance close to optimal for both parameters. Closed-form expressions are given for the covariance matrixof the estimator as a function of data length, and are shown by simulationto be very accurate even when the technical idealizations are not satisfied, Comparisons are made against two maximum-likelihood estimators. In termsof robustness and computational cost the wavelet estimator is found to be clearly superior and statistically its performance is comparable, We apply the tool to the analysis of Ethernet teletraffic data, completing an earlier study on the scaling parameter alone.

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Documento generato il 01/10/20 alle ore 00:14:13