Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics

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dc.catalogadorjca
dc.contributor.authorMarmolejo-Ramos, Fernando
dc.contributor.authorTejo, Mauricio
dc.contributor.authorBrabec, Marek
dc.contributor.authorKuzilek, Jakub
dc.contributor.authorJoksimovic, Srecko
dc.contributor.authorKovanovic, Vitomir
dc.contributor.authorGonzalez Burgos, Jorge
dc.contributor.authorKneib, Thomas
dc.contributor.authorBühlmann, Peter
dc.contributor.authorKook, Lucas
dc.contributor.authorBriseño Sánchez, Guillermo
dc.contributor.authorOspina, Raydonal
dc.date.accessioned2023-12-20T13:10:14Z
dc.date.available2023-12-20T13:10:14Z
dc.date.issued2022
dc.description.abstractThe advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Statistics Technologies > Machine Learning.
dc.description.funderINICI‐UV
dc.description.funderInstitute of Computer Science
dc.description.funderNovartis Research Foundation
dc.description.funderH2020 European Research Council
dc.description.funderEuropean Research Council
dc.description.funderDeutsche Forschungsgemeinschaft
dc.description.funderGrantová Agentura České Republiky
dc.description.funderFondo Nacional de Desarrollo Científico y Tecnológico
dc.description.funderConselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.funderUniversidad de Valparaíso
dc.fechaingreso.objetodigital2023-12-20
dc.fuente.origenORCID
dc.identifier.doi10.1002/widm.1479
dc.identifier.eissn1942-24795
dc.identifier.issn19424795 19424787
dc.identifier.scopusidSCOPUS_ID:85140231241
dc.identifier.urihttps://doi.org/10.1002/widm.1479
dc.identifier.urihttp://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1942-4795
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75550
dc.identifier.wosidWOS:000870901000001
dc.information.autorucFacultad de Matemáticas; Gonzalez Burgos, Jorge; 0000-0003-0415-3795; 15102
dc.language.isoen
dc.nota.accesoContenido completo
dc.publisherJohn Wiley and Sons Inc
dc.relation.ispartofWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
dc.revistaWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
dc.rightsacceso abierto
dc.subjectCausal regularization
dc.subjectCausality
dc.subjectEducational data mining
dc.subjectGeneralized additive models for location, scale, and shape
dc.subjectLearning analytics
dc.subjectMachine learning
dc.subjectStatistical learning
dc.subjectStatistical modeling
dc.subjectSupervised learning
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleDistributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
dc.typeartículo
sipa.codpersvinculados15102
sipa.trazabilidadORCID;2023-12-18
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