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

0563344 - ÚI 2024 RIV US eng J - Journal Article
Marmolejo-Ramos, F. - Tejo, M. - Brabec, Marek - Kužílek, J. - Joksimovic, S. - Kovanovic, V. - González, J. - Kneib, T. - Bühlmann, P. - Kook, L. - Briseño-Sánchez, G. - Ospina, R.
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery. Roč. 13, č. 1 (2023), č. článku e1479. ISSN 1942-4787. E-ISSN 1942-4795
Institutional support: RVO:67985807
Keywords : causal regularization * causality * educational data mining * generalized additive models for location, scale and shape * learning analytics * machine learning * statistical learning * statistical modeling * supervised learning
OECD category: Statistics and probability
Impact factor: 7.8, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.1002/widm.1479

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.
Permanent Link: https://hdl.handle.net/11104/0335333