A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models
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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 of 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 concerning the PPZ. It can be easily adapted to many versions of EM.
KeywordsNonlinear mixed effects models Maximum likelihood Expected maximisation algorithm Longitudinal data analysis Repeated measurements Iterated proportional fitting algorithm Gaussian graphical models Stochastic inverse problems Pharmacokinetic/pharmacodynamics analysis
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- Beal, S.L., Sheiner, L.B.: NONMEM user’s guide. Nonlinear mixed effects models for repeated measures data. University of California, San Francisco (1992) Google Scholar
- Cox, D.R., Wermuth, N., Marchetti, G.: Decompositions and estimation of a chain of covariances. Technical Report, Department of Mathematical Statistics, Chalmers Goteborgs Universitet (2004) Google Scholar
- Dahl, J., Vandenberghe, L., Roychowdhury, V.: Covariance selection for non-chordal graphs via chordal embedding. Preprint, available on http://www.ee.ucla.edu/~vandenbe/covsel.html (2006)
- Davidian, M., Giltinan, D.M.: Nonlinear Models for Repeated Measurement Data. Chapman and Hall, New York (1995) Google Scholar