Maximum likelihood estimation in nonlinear mixed effects models

A stochastic approximation version of EM for maximum likelihood estimation of a wide class of nonlinear mixed effects models is proposed. The main advantage of this algorithm is its ability to provide an estimator close to the MLE in very few iterations. The likelihood of the observations as well as...

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Bibliographic Details
Published in:Computational statistics & data analysis 2005-06, Vol.49 (4), p.1020-1038
Main Authors: Kuhn, E., Lavielle, M.
Format: Article
Language:English
Subjects:
EM algorithm
Exact sciences and technology
General topics
Mathematics
Maximum likelihood estimation
Mixed effects model
Multivariate analysis
Nonlinear model
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
SAEM algorithm
Sciences and techniques of general use
Sequential methods
Statistics
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Summary:A stochastic approximation version of EM for maximum likelihood estimation of a wide class of nonlinear mixed effects models is proposed. The main advantage of this algorithm is its ability to provide an estimator close to the MLE in very few iterations. The likelihood of the observations as well as the Fisher Information matrix can also be estimated by stochastic approximations. Numerical experiments allow to highlight the very good performances of the proposed method.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2004.07.002