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Non-linear mixed-effects modeling software

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Nonlinear mixed effects models are a special case of Regression analysis and a range of different software solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a Gauss–Markov theorem impossible. Nonlinear mixed effects models are therefore estimated according to Maximum Likelihood principles.[1]. Specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the lapplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling. A special case is use of non-parametric approaches. Furthermore, estimation in limited or full Bayesian frameworks is performed using the Metropolic-Hastings or the NUTS algorithms.

References

  1. ^ Davidian, Marie; Giltinan, David M. (1995-06-01). Nonlinear Models for Repeated Measurement Data. CRC Press. ISBN 978-0-412-98341-2.
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