Adaptive basis selection for functional data analysis via stochastic penalization

Detalhes bibliográficos
Autor(a) principal: Anselmo,Cezar A.F.
Data de Publicação: 2005
Outros Autores: Dias,Ronaldo, Garcia,Nancy L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Computational & Applied Mathematics
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022005000200004
Resumo: We propose an adaptive method of analyzing a collection of curves which can be, individually, modeled as a linear combination of spline basis functions. Through the introduction of latent Bernoulli variables, the number of basis functions, the variance of the error measurements and the coefficients of the expansion are determined. We provide a modification of the stochastic EM algorithm for which numerical results show that the estimates are very close to the true curve in the sense of L2 norm.
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spelling Adaptive basis selection for functional data analysis via stochastic penalizationbasis functionsSEM algorithmfunctional statisticssummary measuressplinesnon-parametric data analysisregistrationWe propose an adaptive method of analyzing a collection of curves which can be, individually, modeled as a linear combination of spline basis functions. Through the introduction of latent Bernoulli variables, the number of basis functions, the variance of the error measurements and the coefficients of the expansion are determined. We provide a modification of the stochastic EM algorithm for which numerical results show that the estimates are very close to the true curve in the sense of L2 norm.Sociedade Brasileira de Matemática Aplicada e Computacional2005-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022005000200004Computational & Applied Mathematics v.24 n.2 2005reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMAC10.1590/S0101-82052005000200004info:eu-repo/semantics/openAccessAnselmo,Cezar A.F.Dias,RonaldoGarcia,Nancy L.eng2005-11-07T00:00:00Zoai:scielo:S1807-03022005000200004Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2005-11-07T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false
dc.title.none.fl_str_mv Adaptive basis selection for functional data analysis via stochastic penalization
title Adaptive basis selection for functional data analysis via stochastic penalization
spellingShingle Adaptive basis selection for functional data analysis via stochastic penalization
Anselmo,Cezar A.F.
basis functions
SEM algorithm
functional statistics
summary measures
splines
non-parametric data analysis
registration
title_short Adaptive basis selection for functional data analysis via stochastic penalization
title_full Adaptive basis selection for functional data analysis via stochastic penalization
title_fullStr Adaptive basis selection for functional data analysis via stochastic penalization
title_full_unstemmed Adaptive basis selection for functional data analysis via stochastic penalization
title_sort Adaptive basis selection for functional data analysis via stochastic penalization
author Anselmo,Cezar A.F.
author_facet Anselmo,Cezar A.F.
Dias,Ronaldo
Garcia,Nancy L.
author_role author
author2 Dias,Ronaldo
Garcia,Nancy L.
author2_role author
author
dc.contributor.author.fl_str_mv Anselmo,Cezar A.F.
Dias,Ronaldo
Garcia,Nancy L.
dc.subject.por.fl_str_mv basis functions
SEM algorithm
functional statistics
summary measures
splines
non-parametric data analysis
registration
topic basis functions
SEM algorithm
functional statistics
summary measures
splines
non-parametric data analysis
registration
description We propose an adaptive method of analyzing a collection of curves which can be, individually, modeled as a linear combination of spline basis functions. Through the introduction of latent Bernoulli variables, the number of basis functions, the variance of the error measurements and the coefficients of the expansion are determined. We provide a modification of the stochastic EM algorithm for which numerical results show that the estimates are very close to the true curve in the sense of L2 norm.
publishDate 2005
dc.date.none.fl_str_mv 2005-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022005000200004
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022005000200004
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0101-82052005000200004
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
dc.source.none.fl_str_mv Computational & Applied Mathematics v.24 n.2 2005
reponame:Computational & Applied Mathematics
instname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
instacron:SBMAC
instname_str Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
instacron_str SBMAC
institution SBMAC
reponame_str Computational & Applied Mathematics
collection Computational & Applied Mathematics
repository.name.fl_str_mv Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)
repository.mail.fl_str_mv ||sbmac@sbmac.org.br
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