Nonlinear random effects mixture models: maximum likelihood estimation via the EM algorithm

DOI10.1016/j.csda.2007.03.008zbMath1445.62297OpenAlexW2159677816WikidataQ37344816 ScholiaQ37344816MaRDI QIDQ1020774

Alan Schumitzky, David Z. D'Argenio, Xiao-Ning Wang

Publication date: 2 June 2009

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

Full work available at URL: http://europepmc.org/articles/pmc2743159

zbMATH Keywords

finite mixture modelsmixed effects modelspharmacokinetics/pharmacodynamics

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10)

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Uses Software

• NONMEM

Cites Work

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• Maximum likelihood estimation in nonlinear mixed effects models
• On the convergence properties of the EM algorithm
• Asymptotic normality of the maximum likelihood estimate in the independent not identically distributed case
• The nonlinear mixed effects model with a smooth random effects density
• An EM Algorithm for Nonlinear Random Effects Models
• A maximum likelihood estimation method for random coefficient regression models
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• An Analysis of the EM Algorithm and Entropy-Like Proximal Point Methods

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