Maximum likelihood estimation in nonlinear mixed effects models

Authors:
E. Kuhn;M. Lavielle
Affiliations:
Université Paris Sud, Bít. 425, 91405 Orsay, France;Université Paris Sud, Bít. 425, 91405 Orsay, France
Venue:
Computational Statistics & Data Analysis
Year:
2005

Citing 2
Cited 18

A simulated annealing version of the EM algorithm for non-Gaussian deconvolution

Statistics and Computing
A simulated pseudo-maximum likelihood estimator for nonlinear mixed models

Computational Statistics & Data Analysis

Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model

Computational Statistics & Data Analysis
EM algorithms for nonlinear mixed effects models

Computational Statistics & Data Analysis
A parameter expansion version of the SAEM algorithm

Statistics and Computing
Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm

Computational Statistics & Data Analysis
A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models

Statistics and Computing
An implementation of the Expectation-Maximisation (EM) algorithm for population pharmacokinetic-pharmacodynamic modelling in ACSLXTREME

Computer Methods and Programs in Biomedicine
Inference in HIV dynamics models via hierarchical likelihood

Computational Statistics & Data Analysis
A numerical method for minimum distance estimation problems

Journal of Multivariate Analysis
Parametric approximation of functions using genetic algorithms: an example with a logistic curve

NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
Computing and estimating information matrices of weak ARMA models

Computational Statistics & Data Analysis
Estimation in nonlinear mixed-effects models using heavy-tailed distributions

Statistics and Computing
Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm

Computational Statistics & Data Analysis
An empirical Bayes procedure for the selection of Gaussian graphical models

Statistics and Computing
On a convergent stochastic estimation algorithm for frailty models

Statistics and Computing
NIMROD: A program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations

Computer Methods and Programs in Biomedicine
Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm

Computational Statistics & Data Analysis
Nonlinear nonparametric mixed-effects models for unsupervised classification

Computational Statistics
Estimating mixed-effects differential equation models

Statistics and Computing

Quantified Score

Hi-index	0.04

Visualization

Abstract

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.