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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00563344" target="_blank" >RIV/67985807:_____/23:00563344 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00361699

Výsledek na webu

<a href="https://dx.doi.org/10.1002/widm.1479" target="_blank" >https://dx.doi.org/10.1002/widm.1479</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/widm.1479" target="_blank" >10.1002/widm.1479</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Prediktivní modelování studentova výkonu s využitím výukových zdrojů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

ISSN

1942-4787

e-ISSN

1942-4795

Svazek periodika

13

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

e1479

Kód UT WoS článku

000870901000001

EID výsledku v databázi Scopus

2-s2.0-85140231241