A comparison of methods for clustering longitudinal data with slowly changing trends

DOI10.1080/03610918.2020.1861464OpenAlexW3122034724MaRDI QIDQ6171516

Edwin R. van den Heuvel, Unnamed Author, Unnamed Author

Publication date: 18 July 2023

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2020.1861464

zbMATH Keywords

simulation studyintensive longitudinal datagroup-based trajectory modelinggrowth mixture modelinglatent-class trajectory modelinglongitudinal clustering

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Random-Effects Models for Longitudinal Data
• New approaches in classification and data analysis
• The effect of model misspecification on growth mixture model class enumeration
• KmL: k-means for longitudinal data
• A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
• Mersenne twister
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• On the performance of MixTVEM: a simulation study
• General growth mixture modeling for randomized preventive interventions
• Longitudinal Data Analysis
• Linear mixed models for longitudinal data

This page was built for publication: A comparison of methods for clustering longitudinal data with slowly changing trends