Staged trees for discrete longitudinal data

In this paper we investigate the use of staged tree models for discrete longitudinal data. Staged trees are a type of probabilistic graphical model for finite sample space processes. They are a natural fit for longitudinal data because a temporal ordering is often implicitly assumed and standard met...

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Detalles Bibliográficos
Autores: Carter, Jack, Leonelli, Manuele, Riccomagno, Eva, Ugolini, Alessandro
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/3905
Acceso en línea:https://doi.org/10.1007/s00184-024-00987-9
https://hdl.handle.net/20.500.14417/3905
https://link.springer.com/article/10.1007/s00184-024-00987-9
Access Level:acceso abierto
Palabra clave:Chain event graphs
Discrete data
Longitudinal studies
Staged trees
33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
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spelling Staged trees for discrete longitudinal dataCarter, JackLeonelli, ManueleRiccomagno, EvaUgolini, AlessandroChain event graphsDiscrete dataLongitudinal studiesStaged trees33 Ciencias TecnológicasODS 9 - Industria, innovación e infraestructuraIn this paper we investigate the use of staged tree models for discrete longitudinal data. Staged trees are a type of probabilistic graphical model for finite sample space processes. They are a natural fit for longitudinal data because a temporal ordering is often implicitly assumed and standard methods can be used for model selection and probability estimation. However, model selection methods perform poorly when the sample size is small relative to the size of the graph and model interpretation is tricky with larger graphs. This is exacerbated by longitudinal data which is characterized by repeated observations. To address these issues we propose two approaches: the longitudinal staged tree with Markov assumptions which makes some initial conditional independence assumptions represented by a directed acyclic graph and marginal longitudinal staged trees which model certain margins of the data.yesPublishedSpringer Naturehttps://ror.org/02jjdwm7520252025info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1007/s00184-024-00987-9https://hdl.handle.net/20.500.14417/3905https://link.springer.com/article/10.1007/s00184-024-00987-9reponame:Repositorio IEinstname:IEInglésIE School of Science & TechnologyIE UniversityApplied MathematicsAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/deedinfo:eu-repo/semantics/openAccessoai:repositorio.ie.edu:20.500.14417/39052026-06-15T12:40:57Z
dc.title.none.fl_str_mv Staged trees for discrete longitudinal data
title Staged trees for discrete longitudinal data
spellingShingle Staged trees for discrete longitudinal data
Carter, Jack
Chain event graphs
Discrete data
Longitudinal studies
Staged trees
33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
title_short Staged trees for discrete longitudinal data
title_full Staged trees for discrete longitudinal data
title_fullStr Staged trees for discrete longitudinal data
title_full_unstemmed Staged trees for discrete longitudinal data
title_sort Staged trees for discrete longitudinal data
dc.creator.none.fl_str_mv Carter, Jack
Leonelli, Manuele
Riccomagno, Eva
Ugolini, Alessandro
author Carter, Jack
author_facet Carter, Jack
Leonelli, Manuele
Riccomagno, Eva
Ugolini, Alessandro
author_role author
author2 Leonelli, Manuele
Riccomagno, Eva
Ugolini, Alessandro
author2_role author
author
author
dc.contributor.none.fl_str_mv https://ror.org/02jjdwm75
dc.subject.none.fl_str_mv Chain event graphs
Discrete data
Longitudinal studies
Staged trees
33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
topic Chain event graphs
Discrete data
Longitudinal studies
Staged trees
33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
description In this paper we investigate the use of staged tree models for discrete longitudinal data. Staged trees are a type of probabilistic graphical model for finite sample space processes. They are a natural fit for longitudinal data because a temporal ordering is often implicitly assumed and standard methods can be used for model selection and probability estimation. However, model selection methods perform poorly when the sample size is small relative to the size of the graph and model interpretation is tricky with larger graphs. This is exacerbated by longitudinal data which is characterized by repeated observations. To address these issues we propose two approaches: the longitudinal staged tree with Markov assumptions which makes some initial conditional independence assumptions represented by a directed acyclic graph and marginal longitudinal staged trees which model certain margins of the data.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s00184-024-00987-9
https://hdl.handle.net/20.500.14417/3905
https://link.springer.com/article/10.1007/s00184-024-00987-9
url https://doi.org/10.1007/s00184-024-00987-9
https://hdl.handle.net/20.500.14417/3905
https://link.springer.com/article/10.1007/s00184-024-00987-9
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IE School of Science & Technology
IE University
Applied Mathematics
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/deed
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/deed
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:Repositorio IE
instname:IE
instname_str IE
reponame_str Repositorio IE
collection Repositorio IE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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