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

Marmolejo-Ramos, Fernando; Tejo, Mauricio; Brabec, Marek; Kuzilek, Jakub; Joksimovic, Srecko; Kovanovic, Vitomir; GONZALEZ, J; Kneib, T.; Buehlmann, Peter; Kook, Lucas; Briseno-Sanchez, Guillermo; 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. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Statistics Technologies > Machine Learning

Más información

Título según WOS: Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
Título según SCOPUS: ID SCOPUS_ID:85140231241 Not found in local SCOPUS DB
Título de la Revista: Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Volumen: 13
Fecha de publicación: 2023
DOI:

10.1002/WIDM.1479

Notas: ISI, SCOPUS - WOS