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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Date
2023
Authors
Marmolejo‐Ramos, F.
Tejo, M.
Brabec, M.
Kuzilek, J.
Joksimovic, S.
Kovanovic, V.
González, J.
Kneib, T.
Bühlmann, P.
Kook, L.
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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2023; 13(1, article no. e1479):1-22
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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.
This article is categorized under: Application Areas - Education and Learning; Algorithmic Development - Statistics; Technologies - Machine Learning.
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Copyright 2022 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
Access Condition Notes: Open access publishing facilitated by University of South Australia, as part of the Wiley - University of South Australia agreement via the Council of Australian University Librarians.