Scaling range automated selection for wavelet leader multifractal analysis

Autores
Leonarduzzi, Roberto Fabio; Torres, Maria Eugenia; Abry, Patrice
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Scale invariance and multifractal analysis constitute paradigms nowadays widely used for real-world data characterization. In essence, they amount to assuming power law behaviors of well-chosen multiresolution quantities as functions of the analysis scale. The exponents of these power laws, the scaling exponents, are then measured and involved in classical signal processing tasks. Yet, the practical estimation of such exponents implies the selection of a range of scales where the power law behaviors hold, a difficult task with yet crucial impact on performance. In the present contribution, a nonparametric bootstrap based procedure is devised to achieve scaling range automated selection. It is shown to be effective and relevant in practice. Its performance, benefits and computational costs are assessed by means of Monte Carlo simulations. It is applied to synthetic multifractal processes and shown to yield robust and accurate estimation of multifractal parameters, despite various difficulties such as noise corruption or inter-subject variability. Finally, its potential is illustrated at work for the analysis of adult heart rate variability on a large database.
Fil: Leonarduzzi, Roberto Fabio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Abry, Patrice. Centre National de la Recherche Scientifique; Francia
Materia
AUTOMATED SCALING RANGE SELECTION
BOOTSTRAP
MULTIFRACTAL ANALYSIS
WAVELET LEADERS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/85412
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/85412
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Scaling range automated selection for wavelet leader multifractal analysisLeonarduzzi, Roberto FabioTorres, Maria EugeniaAbry, PatriceAUTOMATED SCALING RANGE SELECTIONBOOTSTRAPMULTIFRACTAL ANALYSISWAVELET LEADERShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Scale invariance and multifractal analysis constitute paradigms nowadays widely used for real-world data characterization. In essence, they amount to assuming power law behaviors of well-chosen multiresolution quantities as functions of the analysis scale. The exponents of these power laws, the scaling exponents, are then measured and involved in classical signal processing tasks. Yet, the practical estimation of such exponents implies the selection of a range of scales where the power law behaviors hold, a difficult task with yet crucial impact on performance. In the present contribution, a nonparametric bootstrap based procedure is devised to achieve scaling range automated selection. It is shown to be effective and relevant in practice. Its performance, benefits and computational costs are assessed by means of Monte Carlo simulations. It is applied to synthetic multifractal processes and shown to yield robust and accurate estimation of multifractal parameters, despite various difficulties such as noise corruption or inter-subject variability. Finally, its potential is illustrated at work for the analysis of adult heart rate variability on a large database.Fil: Leonarduzzi, Roberto Fabio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Abry, Patrice. Centre National de la Recherche Scientifique; FranciaElsevier Science2014-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/85412Leonarduzzi, Roberto Fabio; Torres, Maria Eugenia; Abry, Patrice; Scaling range automated selection for wavelet leader multifractal analysis; Elsevier Science; Signal Processing; 105; 12-2014; 243-2570165-1684CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168414002680info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2014.06.002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:03Zoai:ri.conicet.gov.ar:11336/85412instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:51:04.138CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Scaling range automated selection for wavelet leader multifractal analysis
title Scaling range automated selection for wavelet leader multifractal analysis
spellingShingle Scaling range automated selection for wavelet leader multifractal analysis
Leonarduzzi, Roberto Fabio
AUTOMATED SCALING RANGE SELECTION
BOOTSTRAP
MULTIFRACTAL ANALYSIS
WAVELET LEADERS
title_short Scaling range automated selection for wavelet leader multifractal analysis
title_full Scaling range automated selection for wavelet leader multifractal analysis
title_fullStr Scaling range automated selection for wavelet leader multifractal analysis
title_full_unstemmed Scaling range automated selection for wavelet leader multifractal analysis
title_sort Scaling range automated selection for wavelet leader multifractal analysis
dc.creator.none.fl_str_mv Leonarduzzi, Roberto Fabio
Torres, Maria Eugenia
Abry, Patrice
author Leonarduzzi, Roberto Fabio
author_facet Leonarduzzi, Roberto Fabio
Torres, Maria Eugenia
Abry, Patrice
author_role author
author2 Torres, Maria Eugenia
Abry, Patrice
author2_role author
author
dc.subject.none.fl_str_mv AUTOMATED SCALING RANGE SELECTION
BOOTSTRAP
MULTIFRACTAL ANALYSIS
WAVELET LEADERS
topic AUTOMATED SCALING RANGE SELECTION
BOOTSTRAP
MULTIFRACTAL ANALYSIS
WAVELET LEADERS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Scale invariance and multifractal analysis constitute paradigms nowadays widely used for real-world data characterization. In essence, they amount to assuming power law behaviors of well-chosen multiresolution quantities as functions of the analysis scale. The exponents of these power laws, the scaling exponents, are then measured and involved in classical signal processing tasks. Yet, the practical estimation of such exponents implies the selection of a range of scales where the power law behaviors hold, a difficult task with yet crucial impact on performance. In the present contribution, a nonparametric bootstrap based procedure is devised to achieve scaling range automated selection. It is shown to be effective and relevant in practice. Its performance, benefits and computational costs are assessed by means of Monte Carlo simulations. It is applied to synthetic multifractal processes and shown to yield robust and accurate estimation of multifractal parameters, despite various difficulties such as noise corruption or inter-subject variability. Finally, its potential is illustrated at work for the analysis of adult heart rate variability on a large database.
Fil: Leonarduzzi, Roberto Fabio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
Fil: Abry, Patrice. Centre National de la Recherche Scientifique; Francia
description Scale invariance and multifractal analysis constitute paradigms nowadays widely used for real-world data characterization. In essence, they amount to assuming power law behaviors of well-chosen multiresolution quantities as functions of the analysis scale. The exponents of these power laws, the scaling exponents, are then measured and involved in classical signal processing tasks. Yet, the practical estimation of such exponents implies the selection of a range of scales where the power law behaviors hold, a difficult task with yet crucial impact on performance. In the present contribution, a nonparametric bootstrap based procedure is devised to achieve scaling range automated selection. It is shown to be effective and relevant in practice. Its performance, benefits and computational costs are assessed by means of Monte Carlo simulations. It is applied to synthetic multifractal processes and shown to yield robust and accurate estimation of multifractal parameters, despite various difficulties such as noise corruption or inter-subject variability. Finally, its potential is illustrated at work for the analysis of adult heart rate variability on a large database.
publishDate 2014
dc.date.none.fl_str_mv 2014-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/85412
Leonarduzzi, Roberto Fabio; Torres, Maria Eugenia; Abry, Patrice; Scaling range automated selection for wavelet leader multifractal analysis; Elsevier Science; Signal Processing; 105; 12-2014; 243-257
0165-1684
CONICET Digital
CONICET
url http://hdl.handle.net/11336/85412
identifier_str_mv Leonarduzzi, Roberto Fabio; Torres, Maria Eugenia; Abry, Patrice; Scaling range automated selection for wavelet leader multifractal analysis; Elsevier Science; Signal Processing; 105; 12-2014; 243-257
0165-1684
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168414002680
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sigpro.2014.06.002
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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