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Alternative Cholesky Decomposition and family of scale mixture of Normal distribution: A joint modeling approach

dc.rights.licenseopenen_US
dc.contributor.authorRIBEIRO, Vinícius Silva Osterne
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
hal.structure.identifierEcole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine [Bordeaux Sciences Agro]
dc.contributor.authorBOMBRUN, Lionel
ORCID: 0000-0001-9036-3988
IDREF: 137837461
dc.contributor.authorNOBRE, Juvêncio Santos
dc.contributor.authorCAVALCANTE, Charles Casimiro
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorBERTHOUMIEU, Yannick
dc.date.accessioned2025-12-15T07:49:00Z
dc.date.available2025-12-15T07:49:00Z
dc.date.issued2026-01
dc.identifier.issn0165-1684en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/207962
dc.description.abstractEnIn Statistics, the analysis of longitudinal data is essential across various domains, including biomedical and agricultural research. Joint mean-covariance models have been widely used to capture within-subject dependence, often by parametrizing the scatter matrix via the Modified Cholesky Decomposition (MCD). However, the MCD has known drawbacks, such as sensitivity to the ordering of variables and challenges in parameter interpretation. As an alternative, the Alternative Cholesky Decomposition (ACD) offers improved numerical stability and interpretability, yet has been underexplored in robust modeling contexts. Traditional approaches also frequently assume normally distributed residuals, which may not hold in practice. While extensions based on the Student-t and Laplace distributions address heavier tails, they still rely on fixed parametric forms. To overcome both structural and distributional limitations, this paper proposes a novel joint regression model that combines the flexibility of ACD with the robustness of scale mixture of normal (SMN) distributions. We obtain maximum likelihood estimators and compare our model against classical and Student-t-based alternatives. Simulation studies show superior performance in estimation and prediction under outlier contamination. Real data applications further highlight the model’s robustness and practical utility.
dc.language.isoENen_US
dc.subject.enRepeated measures
dc.subject.enScale mixture of normal distribution
dc.subject.enCholesky
dc.subject.enDecomposition
dc.subject.enRobust estimation
dc.title.enAlternative Cholesky Decomposition and family of scale mixture of Normal distribution: A joint modeling approach
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.sigpro.2025.110207en_US
dc.subject.halInformatique [cs]/Traitement du signal et de l'imageen_US
bordeaux.journalSignal Processingen_US
bordeaux.page110207en_US
bordeaux.volume238en_US
bordeaux.hal.laboratoriesIMS : Laboratoire de l'Intégration du Matériau au Système - UMR 5218en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.teamSIGNAL AND IMAGE PROCESSINGen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcecrossref
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
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dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Signal%20Processing&rft.date=2026-01&rft.volume=238&rft.spage=110207&rft.epage=110207&rft.eissn=0165-1684&rft.issn=0165-1684&rft.au=RIBEIRO,%20Vin%C3%ADcius%20Silva%20Osterne&BOMBRUN,%20Lionel&NOBRE,%20Juv%C3%AAncio%20Santos&CAVALCANTE,%20Charles%20Casimiro&BERTHOUMIEU,%20Yannick&rft.genre=article


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