Mixture models for clustering multilevel growth trajectories

DOI10.1016/j.csda.2012.12.007zbMath1471.62149OpenAlexW2049917617MaRDI QIDQ1621280

R. Smith

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.12.007

zbMATH Keywords

EM algorithmrandom effectsmixture modelsmultilevel growth trajectories

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (8)

Editorial: The 2nd special issue on advances in mixture models ⋮ Grouped Generalized Estimating Equations for Longitudinal Data Analysis ⋮ Extending finite mixtures of \(t\) linear mixed-effects models with concomitant covariates ⋮ Health and Income: Theory and Evidence for OECD Countries ⋮ Multi-dimensional latent group structures with heterogeneous distributions ⋮ Heterogeneous analysis for clustered data using grouped finite mixture models ⋮ Grouped Heterogeneous Mixture Modeling for Clustered Data ⋮ Latent mixture modeling for clustered data

Uses Software

• fda (R)

Cites Work

• Handbook of computational statistics. Concepts and methods.
• Finite mixtures of multivariate Poisson distributions with application
• Functional data analysis.
• Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
• The EM Algorithm and Extensions, 2E
• Analyzing Microarray Gene Expression Data
• Clustering for Sparsely Sampled Functional Data
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