Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models

Detalhes bibliográficos
Autor(a) principal: Faria, Susana
Data de Publicação: 2008
Outros Autores: Soromenho, Gilda
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10451/4713
Resumo: In this work, we propose to compare two algorithms to compute maximum likelihood estimators of the parameters of a mixture Poisson regression models. To estimate these parameters, we may use the EM algorithm in a mixture approach or the CEM algorithm in a classification approach. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets in a target number of iterations. Simulation results show that the CEM algorithm is a good alternative to the EM algorithm for fitting Poisson mixture regression models, having the advantage of converging more quickly.
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spelling Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression ModelsSimulation studyEM algorithmMixture Poisson Regression ModelsClassification EM algorithmIn this work, we propose to compare two algorithms to compute maximum likelihood estimators of the parameters of a mixture Poisson regression models. To estimate these parameters, we may use the EM algorithm in a mixture approach or the CEM algorithm in a classification approach. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets in a target number of iterations. Simulation results show that the CEM algorithm is a good alternative to the EM algorithm for fitting Poisson mixture regression models, having the advantage of converging more quickly.Repositório da Universidade de LisboaFaria, SusanaSoromenho, Gilda2011-12-27T14:40:39Z2008-082008-08-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10451/4713engCompstat 2008-Proceedings in Computational Statistics, Vol. 2info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T12:47:40Zoai:repositorio.ulisboa.pt:10451/4713Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T02:27:56.073754Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
title Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
spellingShingle Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
Faria, Susana
Simulation study
EM algorithm
Mixture Poisson Regression Models
Classification EM algorithm
title_short Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
title_full Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
title_fullStr Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
title_full_unstemmed Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
title_sort Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
author Faria, Susana
author_facet Faria, Susana
Soromenho, Gilda
author_role author
author2 Soromenho, Gilda
author2_role author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Faria, Susana
Soromenho, Gilda
dc.subject.por.fl_str_mv Simulation study
EM algorithm
Mixture Poisson Regression Models
Classification EM algorithm
topic Simulation study
EM algorithm
Mixture Poisson Regression Models
Classification EM algorithm
description In this work, we propose to compare two algorithms to compute maximum likelihood estimators of the parameters of a mixture Poisson regression models. To estimate these parameters, we may use the EM algorithm in a mixture approach or the CEM algorithm in a classification approach. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets in a target number of iterations. Simulation results show that the CEM algorithm is a good alternative to the EM algorithm for fitting Poisson mixture regression models, having the advantage of converging more quickly.
publishDate 2008
dc.date.none.fl_str_mv 2008-08
2008-08-01T00:00:00Z
2011-12-27T14:40:39Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/4713
url http://hdl.handle.net/10451/4713
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Compstat 2008-Proceedings in Computational Statistics, Vol. 2
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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