Clustering in linear-mixed models with a group fused lasso penalty

DOI10.1002/bimj.201200111zbMath1280.62076OpenAlexW2136181827WikidataQ44902789 ScholiaQ44902789MaRDI QIDQ2874292

Felix Heinzl, Gerhard Tutz

Publication date: 29 January 2014

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201200111

zbMATH Keywords

EM algorithmlongitudinal datagroup lassofused lassolinear-mixed models

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Additive mixed models with approximate Dirichlet process mixtures: the EM approach, Unsupervised learning of mixture regression models for longitudinal data, A Bayesian approach to disease clustering using restricted Chinese restaurant processes, Random effects clustering in multilevel modeling: choosing a proper partition, Tree-Structured Clustering in Fixed Effects Models, A modified generalized Lasso algorithm to detect local spatial clusters for count data

Uses Software

• mixAK
• R
• AS 47
• lme4
• SemiPar
• ASA047

Cites Work

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