ReviewWiley interdisciplinary reviews. Data mining and knowledge discovery
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.
Review in Wiley interdisciplinary reviews. Data mining and knowledge discovery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Establishment of Thyroid Volume Reference Curves for Children in Xinjiang: An Exploration of Goiter Diagnosis and the Role of Iodine Nutrition.Biological trace element research · 2026Article
- Defining the Therapeutic Range of Levetiracetam for Personalized Treatment in Pediatric Epilepsy: Insights from a Large Real-World Study.Drug design, development and therapy · 2026Article
- The cardiovascular exercise response in children with overweight or obesity measured by cardiovascular magnetic resonance imaging.International journal of obesity (2005) · 2024Article
- In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men.Scientific reports · 2023Article
- Visual search and childhood vision impairment: A GAMLSS-oriented multiverse analysis approach.Attention, perception & psychophysics · 2023Article
- 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 discoveryReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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 LearningAlgorithmic Development > StatisticsTechnologies > Machine Learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.