TY  - JOUR
ID  - eprints1434
EP  - 20
VL  - 2012
TI  - Asymptotic Normality of a Hurst Parameter Estimator Based on the Modified Allan Variance
AV  - none
KW  - modified Allan variance
KW  -  log-regression estimator
KW  -  fractional Brownian motion
KW  -  long-range dependence
KW  -  self-similarity
Y1  - 2012///
UR  - http://www.hindawi.com/journals/ijsa/2012/905082/
JF  - International Journal of Stochastic Analysis
A1  - Bianchi, Alessandra
A1  - Campanino, Massimo
A1  - Crimaldi, Irene
SN  - 2090-3332
PB  - Hindawi Publishing Corporation
N2  - 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.
SP  - 1
ER  -