Maximum likelihood estimation in nonlinear mixed effects models

被引：370

作者：

Kuhn, E ^{[1
]}

Lavielle, M ^{[1
]}

机构：

[1] Univ Paris 11, F-91405 Orsay, France

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2005年 / 49卷 / 04期

关键词：

mixed effects model; nonlinear model; maximum likelihood estimation; EM algorithm; SAEM algorithm;

D O I：

10.1016/j.csda.2004.07.002

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：1020 / 1038

页数：19

共 15 条

[1] A simulated pseudo-maximum likelihood estimator for nonlinear mixed models [J].