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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPHILIPPS, Viviane
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPRAGUE, Melanie
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOMMENGES, Daniel
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X
IDREF: 114375747
dc.date.accessioned2023-06-29T07:24:17Z
dc.date.available2023-06-29T07:24:17Z
dc.date.issued2021-12
dc.identifier.issn2073-4859en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/183236
dc.description.abstractEnImplementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents con vergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in complex settings by allowing parallel calculations at each iteration. We demonstrate through a variety of cases from the literature that our implementation reli ably and consistently reaches the optimum (even when other optimizers fail) and also largely reduces computational time in complex settings through the example of maximum likelihood estimation of different sophisticated statistical models.
dc.description.sponsorshipModèles Dynamiques pour les Etudes Epidémiologiques Longitudinales sur les Maladies Chroniques - ANR-18-CE36-0004en_US
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.title.enRobust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg
dc.typeArticle de revueen_US
dc.identifier.doi10.32614/RJ-2021-089en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.journalR Journalen_US
bordeaux.page365-379en_US
bordeaux.volume13en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue2en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionINRIA
bordeaux.teamSISTM_BPHen_US
bordeaux.teamBIOSTAT_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=R%20Journal&rft.date=2021-12&rft.volume=13&rft.issue=2&rft.spage=365-379&rft.epage=365-379&rft.eissn=2073-4859&rft.issn=2073-4859&rft.au=PHILIPPS,%20Viviane&HEJBLUM,%20Boris&PRAGUE,%20Melanie&COMMENGES,%20Daniel&PROUST%20LIMA,%20Cecile&rft.genre=article


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