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Wavelet entropy of stochastic processes

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Autor dc.contributor.author Zunino L.
Autor dc.contributor.author Perez D.G.
Autor dc.contributor.author Garavaglia M.
Autor dc.contributor.author Rosso O.A.
Fecha Ingreso dc.date.accessioned 2014-04-05T00:15:46Z
Fecha Disponible dc.date.available 2014-04-05T00:15:46Z
Fecha en Repositorio dc.date.issued 2014-04-04
dc.identifier 10.1016/j.physa.2006.12.057
dc.description.abstract We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the normalized total wavelet entropy (NTWS) family [S. Blanco, A. Figliola, R.Q. Quiroga, O.A. Rosso, E. Serrano, Time-frequency analysis of electroencephalogram series, III. Wavelet packets and information cost function, Phys. Rev. E 57 (1998) 932-940; O.A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Başar, Wavelet entropy: a new tool for analysis of short duration brain electrical signals, J. Neurosci. Method 105 (2001) 65-75] and a second introduced by Tavares and Lucena [Physica A 357(1) (2005) 71-78]. In order to understand their advantages and disadvantages, exact results obtained for fractional Gaussian noise (- 1 < α < 1) and fractional Brownian motion (1 < α < 3) are assessed. We find out that the NTWS family performs better as a characterization method for these stochastic processes. © 2007 Elsevier B.V. All rights reserved. en_US
dc.source Physica A: Statistical Mechanics and its Applications
Link Descarga dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-34247543875&partnerID=40&md5=b5dc6f5740866e9a313b8c9712c58a5a
Title dc.title Wavelet entropy of stochastic processes en_US
Tipo dc.type Article
dc.description.keywords Electroencephalography; Frequency domain analysis; Function evaluation; Gaussian noise (electronic); Time domain analysis; Wavelet analysis; α-parameter; Fractional Brownian motion; Fractional Gaussian noise; Wavelet entropy; Brownian movement en_US


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