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

dc.creatorChafai, Djalil
dc.creatorConcordet, Didier
dc.date2007-09-02
dc.date2008-11-25
dc.date.accessioned2026-07-07T12:39:37Z
dc.date.available2026-07-07T12:39:37Z
dc.descriptionWe 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.
dc.descriptionAccepted for publication in Statistics and Computing
dc.identifierhttps://arxiv.org/abs/0709.0111
dc.identifierhttp://arxiv.org/abs/0709.0111
dc.identifierStatistics and Computing 19, 2 (2009) 129-138
dc.identifierdoi:10.1007/s11222-008-9076-9
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/219173
dc.subjectMethodology
dc.subject62F10; 65C60
dc.titleA new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models
dc.typetext

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