Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data

DOI10.4310/SII.2018.v11.n4.a15OpenAlexW2889623748WikidataQ90230588 ScholiaQ90230588MaRDI QIDQ1784735

Sijian Wang, Yun Li, Naisyin Wang, Ji Zhu, Ling Zhou, Peter X.-K. Song

Publication date: 27 September 2018

Published in: Statistics and Its Interface (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4310/sii.2018.v11.n4.a15

zbMATH Keywords

regularizationvariable selectiondiverging raterandom-effects selection

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (4)

A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation ⋮ Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization ⋮ Model selection in linear mixed-effect models ⋮ Sparse linear mixed model selection via streamlined variational Bayes

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