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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification
Hu, Shuwen Wang, You-Gan Drovandi, Christopher Cao, Taoyun
Hu, Shuwen
Wang, You-Gan
Drovandi, Christopher
Cao, Taoyun
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Author
Hu, Shuwen
Wang, You-Gan
Drovandi, Christopher
Cao, Taoyun
Wang, You-Gan
Drovandi, Christopher
Cao, Taoyun
Abstract
We consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model and six different popular machine learning approaches: decision trees, bagging, random forest, boosting, support-vector machine and neural network. In order to consider the correlated data in machine learning, the random effects is combined into the traditional tree methods and random forest. Our focus is the performance of statistical modelling and machine learning especially in the cases of the misspecification of the fixed effects and the random effects. Extensive simulation studies have been carried out to evaluate the performance using a number of criteria. Two real datasets from longitudinal studies are analysed to demonstrate our findings. The R code and dataset are freely available at https://github.com/shuwen92/MEML.
Keywords
longitudinal data, misspecification, machine learning, mixed-effects model, regression tree, support vector machine, comparison study
Date
2023
Type
Journal article
Journal
Statistical Methods and Applications
Book
Volume
32
Issue
2
Page Range
681-711
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
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