Imputation for repeated bounded outcome data: statistical and machine-learning approaches

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dc.contributor.authorAguirre Larracoechea, Urko
dc.contributor.authorBorges Hernández, Cruz E.
dc.date.accessioned2025-09-03T08:31:50Z
dc.date.available2025-09-03T08:31:50Z
dc.date.issued2021-09-28
dc.date.updated2025-09-03T08:31:50Z
dc.description.abstractReal-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.en
dc.description.sponsorshipThis work was partially supported by a research grant from Instituto de Salud Carlos III (PI13/00013, PI18/01589) to U. Aguirre; Department of Health of the Basque Country (2010111098); KRONIKGUNE, Institute for Health Services Research (KRONIK 11/006); and the European Regional Development Fund. These institutions had no further role in study design; in the collection, analysis or interpretation of data; in the writing of the manuscript; or in the decision to submit the paper for publicationen
dc.identifier.citationAguirre-Larracoechea, U., & Borges, C. E. (2021). Imputation for repeated bounded outcome data: statistical and machine-learning approaches. Mathematics, 9(17). https://doi.org/10.3390/MATH9172081
dc.identifier.doi10.3390/MATH9172081
dc.identifier.eissn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3466
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.subject.otherBounded outcomes
dc.subject.otherImputation
dc.subject.otherMachine learning
dc.subject.otherRepeated measures
dc.subject.otherZero-one-inflated beta distribution
dc.titleImputation for repeated bounded outcome data: statistical and machine-learning approachesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue17
oaire.citation.titleMathematics
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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