@article{eprints1434,
          author = {Alessandra Bianchi and Massimo Campanino and Irene Crimaldi},
          volume = {2012},
            year = {2012},
           pages = {1--20},
           title = {Asymptotic Normality of a Hurst Parameter Estimator Based on the Modified Allan Variance},
       publisher = {Hindawi Publishing Corporation},
         journal = {International Journal of Stochastic Analysis},
             url = {http://eprints.imtlucca.it/1434/},
        keywords = {modified Allan variance, log-regression estimator, fractional Brownian motion, long-range dependence, self-similarity},
        abstract = {In order to estimate the memory 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. In this paper we present a rigorous study of the MAVAR log-regression estimator. In particular, under the assumption that the signal process is a fractional Brownian motion, we prove that it is consistent and asymptotically normally distributed. Finally, we discuss its connection with the wavelets estimators.}
}