Identifiability Analysis Using Data Cloning

Detalhes bibliográficos
Autor(a) principal: Sartori Junior , José Augusto
Data de Publicação: 2024
Outros Autores: D’Elia Branco, Márcia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.57805/revstat.v22i1.457
Resumo: Lack of identifiability in statistical models may hinder unique inferential conclusions. Therefore, the search for parametric constraints that ensure identifiability is of utmost importance in statistics. However, for complex modeling strategies, even acquiring the knowledge that the model is unidentifiable may prove very difficult. In this paper, we investigate the use of Data Cloning, a modern algorithm for classical inference in latent variable models, as a tool for assessing model identifiability. We discuss its advantages and disadvantages and illustrate its use with a dynamic linear model.
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spelling Identifiability Analysis Using Data Cloningidentifiabilitydata cloningdynamic modelsMCMC algorithmsLack of identifiability in statistical models may hinder unique inferential conclusions. Therefore, the search for parametric constraints that ensure identifiability is of utmost importance in statistics. However, for complex modeling strategies, even acquiring the knowledge that the model is unidentifiable may prove very difficult. In this paper, we investigate the use of Data Cloning, a modern algorithm for classical inference in latent variable models, as a tool for assessing model identifiability. We discuss its advantages and disadvantages and illustrate its use with a dynamic linear model.Statistics Portugal2024-02-22info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v22i1.457https://doi.org/10.57805/revstat.v22i1.457REVSTAT-Statistical Journal; Vol. 22 No. 1 (2024): REVSTAT-Statistical Journal; 87–110REVSTAT; Vol. 22 N.º 1 (2024): REVSTAT-Statistical Journal; 87–1102183-03711645-6726reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/457https://revstat.ine.pt/index.php/REVSTAT/article/view/457/685Copyright (c) 2024 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessSartori Junior , José AugustoD’Elia Branco, Márcia2024-02-24T07:12:43Zoai:revstat:article/457Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:18.536663Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Identifiability Analysis Using Data Cloning
title Identifiability Analysis Using Data Cloning
spellingShingle Identifiability Analysis Using Data Cloning
Sartori Junior , José Augusto
identifiability
data cloning
dynamic models
MCMC algorithms
title_short Identifiability Analysis Using Data Cloning
title_full Identifiability Analysis Using Data Cloning
title_fullStr Identifiability Analysis Using Data Cloning
title_full_unstemmed Identifiability Analysis Using Data Cloning
title_sort Identifiability Analysis Using Data Cloning
author Sartori Junior , José Augusto
author_facet Sartori Junior , José Augusto
D’Elia Branco, Márcia
author_role author
author2 D’Elia Branco, Márcia
author2_role author
dc.contributor.author.fl_str_mv Sartori Junior , José Augusto
D’Elia Branco, Márcia
dc.subject.por.fl_str_mv identifiability
data cloning
dynamic models
MCMC algorithms
topic identifiability
data cloning
dynamic models
MCMC algorithms
description Lack of identifiability in statistical models may hinder unique inferential conclusions. Therefore, the search for parametric constraints that ensure identifiability is of utmost importance in statistics. However, for complex modeling strategies, even acquiring the knowledge that the model is unidentifiable may prove very difficult. In this paper, we investigate the use of Data Cloning, a modern algorithm for classical inference in latent variable models, as a tool for assessing model identifiability. We discuss its advantages and disadvantages and illustrate its use with a dynamic linear model.
publishDate 2024
dc.date.none.fl_str_mv 2024-02-22
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.57805/revstat.v22i1.457
https://doi.org/10.57805/revstat.v22i1.457
url https://doi.org/10.57805/revstat.v22i1.457
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revstat.ine.pt/index.php/REVSTAT/article/view/457
https://revstat.ine.pt/index.php/REVSTAT/article/view/457/685
dc.rights.driver.fl_str_mv Copyright (c) 2024 REVSTAT-Statistical Journal
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 REVSTAT-Statistical Journal
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Statistics Portugal
publisher.none.fl_str_mv Statistics Portugal
dc.source.none.fl_str_mv REVSTAT-Statistical Journal; Vol. 22 No. 1 (2024): REVSTAT-Statistical Journal; 87–110
REVSTAT; Vol. 22 N.º 1 (2024): REVSTAT-Statistical Journal; 87–110
2183-0371
1645-6726
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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