Classification of longitudinal data through a semiparametric mixed‐effects model based on lasso‐type estimators

DOI10.1111/biom.12280zbMath1390.62233OpenAlexW1884056763WikidataQ30886894 ScholiaQ30886894MaRDI QIDQ3459929

Cristian Meza, Rolando De la Cruz, Ana Arribas-Gil, Emilie Lebarbier

Publication date: 11 January 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10016/32971

zbMATH Keywords

classificationEM algorithmlongitudinal datasemiparametric estimationLassomixed-effects models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (3)

Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization ⋮ Semi-parametric segmentation of multiple series using a DP-Lasso strategy ⋮ Mixtures of stochastic differential equations with random effects: application to data clustering

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