A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models
Methodology
2009-02-11 v2
Abstract
We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists in coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any sample size, and does not rely on any structural assumption on the PPZ. It can be easily adapted to many versions of EM.
Keywords
Cite
@article{arxiv.0709.0111,
title = {A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models},
author = {Djalil Chafai and Didier Concordet},
journal= {arXiv preprint arXiv:0709.0111},
year = {2009}
}
Comments
Accepted for publication in Statistics and Computing