@techreport{eprints1132,
     institution = {IMT Institute for Advanced Studies Lucca},
            year = {2012},
          author = {Alessandra Bianchi and Stefano Bregni and Irene Crimaldi and Marco Ferrari},
            type = {Technical Report},
           title = {Analysis of a Hurst parameter estimator based on the modified Allan variance},
            note = {Electronic preprint, University of Bologna, ISSN 2038-7954},
          number = {3214},
        keywords = {Hurst parameter, long-range dependence, self-similarity, modified Allan variance, parameter estimation, wavelets, fractional Brownian motion},
        abstract = {In order to estimate the Hurst parameter of Internet traffic data, it has been recently proposed a log-regression estimator based on the so-called modified Allan variance(MAVAR). Simulations have shown that this estimator achieves higher accuracy and better confidence when compared with other methods of common use. Here we link it to the wavelets setting and provide an asymptotic analysis in the case the signal process is a fractional Brownian motion. In particular we show that the MAVAR log-regression estimator is consistent and asymptotically normal, providing the related confidence intervals for a suitable choice on the regression weights. Finally, we show some numerical examples.},
             url = {http://eprints.imtlucca.it/1132/}
}