Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
2022 | journal article. A publication with affiliation to the University of Göttingen.
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Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
Marmolejo‐Ramos, F.; Tejo, M.; Brabec, M.; Kuzilek, J.; Joksimovic, S.; Kovanovic, V. & González, J. et al. (2022)
Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery,. DOI: https://doi.org/10.1002/widm.1479
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Details
- Authors
- Marmolejo‐Ramos, Fernando; Tejo, Mauricio; Brabec, Marek; Kuzilek, Jakub; Joksimovic, Srecko; Kovanovic, Vitomir; González, Jorge; Kneib, Thomas ; Bühlmann, Peter; Kook, Lucas; Ospina, Raydonal
- Abstract
- The 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.
- Issue Date
- 2022
- Journal
- Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery
- Organization
- Campus-Institut Data Science ; Professuren für Statistik und Ökonometrie
- ISSN
- 1942-4787
- eISSN
- 1942-4795
- Language
- English
- Sponsor
- Akademie Věd České Republiky https://doi.org/10.13039/501100004240
Conselho Nacional de Desenvolvimento Científico e Tecnológico https://doi.org/10.13039/501100003593
Deutsche Forschungsgemeinschaft https://doi.org/10.13039/501100001659
Fondo Nacional de Desarrollo Científico y Tecnológico https://doi.org/10.13039/501100002850
Grantová Agentura České Republiky https://doi.org/10.13039/501100001824
H2020 European Research Council https://doi.org/10.13039/100010663
Universidad de Valparaíso https://doi.org/10.13039/501100004427