Stochastic proximal-gradient algorithms for penalized mixed models

DOI10.1007/s11222-018-9805-7zbMath1430.62153arXiv1704.08891OpenAlexW2963490248MaRDI QIDQ2329762

Edouard Ollier, Gersende Fort, Adeline Samson

Publication date: 18 October 2019

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.08891

zbMATH Keywords

stochastic approximationstochastic EM algorithmstochastic gradientproximal-gradient algorithmnonlinear mixed effect models

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05)

Related Items

The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution ⋮ Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments ⋮ Fast selection of nonlinear mixed effect models using penalized likelihood

Uses Software

• UNLocBoX
• glmnet

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