Maximum likelihood estimation in nonlinear mixed effects models

E. Kuhn; M. Lavielle

doi:10.1016/j.csda.2004.07.002

Maximum likelihood estimation in nonlinear mixed effects models

E. Kuhn
, M. Lavielle

Université Paris-Saclay

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

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.

langue originale	Anglais
Pages (de - à)	1020-1038
Nombre de pages	19
journal	Computational Statistics and Data Analysis
Volume	49
Numéro de publication	4
Les DOIs	https://doi.org/10.1016/j.csda.2004.07.002
état	Publié - 15 juin 2005
Modification externe	Oui

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10.1016/j.csda.2004.07.002

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title = "Maximum likelihood estimation in nonlinear mixed effects models",

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.",

keywords = "EM algorithm, Maximum likelihood estimation, Mixed effects model, Nonlinear model, SAEM algorithm",

author = "E. Kuhn and M. Lavielle",

year = "2005",

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journal = "Computational Statistics and Data Analysis",

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T1 - Maximum likelihood estimation in nonlinear mixed effects models

AU - Kuhn, E.

AU - Lavielle, M.

PY - 2005/6/15

Y1 - 2005/6/15

N2 - 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.

AB - 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.

KW - EM algorithm

KW - Maximum likelihood estimation

KW - Mixed effects model

KW - Nonlinear model

KW - SAEM algorithm

UR - https://www.scopus.com/pages/publications/19044400906

U2 - 10.1016/j.csda.2004.07.002

DO - 10.1016/j.csda.2004.07.002

M3 - Article

AN - SCOPUS:19044400906

SN - 0167-9473

VL - 49

SP - 1020

EP - 1038

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 4

ER -

Maximum likelihood estimation in nonlinear mixed effects models

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