Open AccessGeneralized Linear Mixed Models by penalized Lasso in modelling the scores of Indonesian students
Author(s) -
Vera Maya Santi,
Khairil Anwar Notodiputro,
Bagus Sartono,
W Rahyu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1869/1/012140
Subject(s) - generalized linear mixed model , covariate , akaike information criterion , generalized linear model , lasso (programming language) , bayesian information criterion , random effects model , mixed model , statistics , econometrics , estimator , mathematics , generalized additive model , linear model , bayesian probability , computer science , medicine , meta analysis , world wide web
The Generalized linear mixed model (GLMM) is an extension of the generalized linear model by adding random effects to linear predictors to accommodate clustered or over dispersion. Severe computational problems in the GLMM modelling cause its use restricted for only a few predictors. When many predictors are available, the estimators become very unstable. Therefore, the procedure for selecting relevant variables is essential in modelling. The use of penalty techniques for selecting variables in mixed models is still rarely applied. In this article, the penalized Lasso approach proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously in GLMM. Based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and standard error criteria, it was found that glmmLasso has a better performance than GLMM. For the factors affecting Indonesian’s student scores, where glmmLasso produces three significant covariates for the GLMM model while GLMM without penalized Lasso has five covariates, which means that the GLMM model is more complicated than glmmLasso. Gender, school quality based on National Examination (UN) scores and the opportunity for students to investigate to test their ideas are essential covariates as factors that influence the rating of Indonesian students.