A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models

Details A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models

2007-09-02

arxiv.org/abs/0709.0111v2

Statistics and Computing 19, 2 (2009) 129-138

Statistics

Methodology

Accepted for publication in Statistics and Computing

Scientific paper

10.1007/s11222-008-9076-9

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.

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