EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions

Autores
Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Flandrin, Patrick
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of iterations is analyzed by means of kernel smoothing density estimations. The obtained results are confirmed by statistical normality tests indicating that the IMFs have non-Gaussian distributions. Our study also indicates that large data length and high number of iterations produce multimodal distributions in all modes.
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Flandrin, Patrick. École Normale Supérieure de Lyon; Francia
Materia
EMPIRICAL MODE DESCOMPOSITION
INTRINSIC MODE FUNTION
GAUSSIAN WHITE NOISE
SIFTING
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:11336/104667
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/104667
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode FunctionsSchlotthauer, GastonTorres, Maria EugeniaRufiner, Hugo LeonardoFlandrin, PatrickEMPIRICAL MODE DESCOMPOSITIONINTRINSIC MODE FUNTIONGAUSSIAN WHITE NOISESIFTINGhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of iterations is analyzed by means of kernel smoothing density estimations. The obtained results are confirmed by statistical normality tests indicating that the IMFs have non-Gaussian distributions. Our study also indicates that large data length and high number of iterations produce multimodal distributions in all modes.Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Flandrin, Patrick. École Normale Supérieure de Lyon; FranciaWorld Scientific Publishing2009-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/104667Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Flandrin, Patrick; EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions; World Scientific Publishing; Advances in Adaptive Data Analysis; 1; 4; 9-2009; 517-5271793-71751793-5369CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.worldscientific.com/doi/abs/10.1142/S1793536909000217info:eu-repo/semantics/altIdentifier/doi/10.1142/S1793536909000217info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-03-31T15:17:11Zoai:ri.conicet.gov.ar:11336/104667instacron: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:34982026-03-31 15:17:11.443CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
title EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
spellingShingle EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
Schlotthauer, Gaston
EMPIRICAL MODE DESCOMPOSITION
INTRINSIC MODE FUNTION
GAUSSIAN WHITE NOISE
SIFTING
title_short EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
title_full EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
title_fullStr EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
title_full_unstemmed EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
title_sort EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions
dc.creator.none.fl_str_mv Schlotthauer, Gaston
Torres, Maria Eugenia
Rufiner, Hugo Leonardo
Flandrin, Patrick
author Schlotthauer, Gaston
author_facet Schlotthauer, Gaston
Torres, Maria Eugenia
Rufiner, Hugo Leonardo
Flandrin, Patrick
author_role author
author2 Torres, Maria Eugenia
Rufiner, Hugo Leonardo
Flandrin, Patrick
author2_role author
author
author
dc.subject.none.fl_str_mv EMPIRICAL MODE DESCOMPOSITION
INTRINSIC MODE FUNTION
GAUSSIAN WHITE NOISE
SIFTING
topic EMPIRICAL MODE DESCOMPOSITION
INTRINSIC MODE FUNTION
GAUSSIAN WHITE NOISE
SIFTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of iterations is analyzed by means of kernel smoothing density estimations. The obtained results are confirmed by statistical normality tests indicating that the IMFs have non-Gaussian distributions. Our study also indicates that large data length and high number of iterations produce multimodal distributions in all modes.
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Flandrin, Patrick. École Normale Supérieure de Lyon; Francia
description This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of iterations is analyzed by means of kernel smoothing density estimations. The obtained results are confirmed by statistical normality tests indicating that the IMFs have non-Gaussian distributions. Our study also indicates that large data length and high number of iterations produce multimodal distributions in all modes.
publishDate 2009
dc.date.none.fl_str_mv 2009-09
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/11336/104667
Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Flandrin, Patrick; EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions; World Scientific Publishing; Advances in Adaptive Data Analysis; 1; 4; 9-2009; 517-527
1793-7175
1793-5369
CONICET Digital
CONICET
url http://hdl.handle.net/11336/104667
identifier_str_mv Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Flandrin, Patrick; EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions; World Scientific Publishing; Advances in Adaptive Data Analysis; 1; 4; 9-2009; 517-527
1793-7175
1793-5369
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.worldscientific.com/doi/abs/10.1142/S1793536909000217
info:eu-repo/semantics/altIdentifier/doi/10.1142/S1793536909000217
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
application/pdf
dc.publisher.none.fl_str_mv World Scientific Publishing
publisher.none.fl_str_mv World Scientific Publishing
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 12.822162

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