eprintid: 1434
rev_number: 9
eprint_status: archive
userid: 36
dir: disk0/00/00/14/34
datestamp: 2012-11-28 13:24:24
lastmod: 2012-11-29 13:17:37
status_changed: 2012-11-28 13:24:24
type: article
metadata_visibility: show
creators_name: Bianchi, Alessandra
creators_name: Campanino, Massimo
creators_name: Crimaldi, Irene
creators_id: 
creators_id: 
creators_id: irene.crimaldi@imtlucca.it
title: Asymptotic Normality of a Hurst Parameter Estimator Based on the Modified Allan Variance
ispublished: pub
subjects: HA
subjects: QA
divisions: EIC
full_text_status: none
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.
date: 2012
date_type: published
publication: International Journal of Stochastic Analysis
volume: 2012
publisher: Hindawi Publishing Corporation
pagerange: 1-20
id_number: 10.1155/2012/905082
refereed: TRUE
issn: 2090-3332
official_url: http://www.hindawi.com/journals/ijsa/2012/905082/
citation:   Bianchi, Alessandra and Campanino, Massimo and Crimaldi, Irene  Asymptotic Normality of a Hurst Parameter Estimator Based on the Modified Allan Variance.  International Journal of Stochastic Analysis, 2012.  pp. 1-20.  ISSN 2090-3332      (2012)