Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE

Parameter estimation in non linear mixed effects models requires a large number of evaluations of the model to study. For ordinary differential equations, the overall computation time remains reasonable. However when the model itself is complex (for instance when it is a set of partial differential equations) it may be time consuming to evaluate it for a single set of parameters. The procedures of population parametrization (for instance using SAEM algorithms) are then very long and in some cases impossible to do within a reasonable time. We propose here a very simple methodology which may accelerate population parametrization of complex models, including partial differential equations models. We illustrate our method on the classical KPP equation.

[1]  Peng Li,et al.  Efficient Look-Up-Table-Based Modeling for Robust Design of $\Sigma\Delta$ ADCs , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  D.J. Rose,et al.  CAzM: A circuit analyzer with macromodeling , 1983, IEEE Transactions on Electron Devices.

[3]  Sophie Donnet,et al.  Parametric inference for mixed models defined by stochastic differential equations , 2008 .

[4]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[5]  R. Punnett,et al.  The Genetical Theory of Natural Selection , 1930, Nature.

[6]  Marc Lavielle,et al.  Estimation of Population Pharmacokinetic Parameters of Saquinavir in HIV Patients with the MONOLIX Software , 2007, Journal of Pharmacokinetics and Pharmacodynamics.

[7]  Sabine Fenstermacher,et al.  Estimation Techniques For Distributed Parameter Systems , 2016 .

[8]  Lei Nie,et al.  Strong Consistency of MLE in Nonlinear Mixed-effects Models with Large Cluster Size , 2005 .

[9]  Niles A. Pierce,et al.  An Introduction to the Adjoint Approach to Design , 2000 .

[10]  S. B. Childs,et al.  INVERSE PROBLEMS IN PARTIAL DIFFERENTIAL EQUATIONS. , 1968 .

[11]  V. Isakov Appendix -- Function Spaces , 2017 .

[12]  Sophie Donnet,et al.  Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations , 2010, Biometrics.

[13]  Marc Lavielle,et al.  Maximum likelihood estimation in nonlinear mixed effects models , 2005, Comput. Stat. Data Anal..

[14]  L. Nie,et al.  Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear and Nonlinear Mixed-Effects Models , 2006 .

[15]  Faming Liang,et al.  Statistical and Computational Inverse Problems , 2006, Technometrics.

[16]  M Lavielle,et al.  A Comprehensive Hepatitis C Viral Kinetic Model Explaining Cure , 2010, Clinical pharmacology and therapeutics.

[17]  J. Lions Optimal Control of Systems Governed by Partial Differential Equations , 1971 .

[18]  H. V. D. Vorst,et al.  Model Order Reduction: Theory, Research Aspects and Applications , 2008 .

[19]  John J. Paulos,et al.  Table-based modeling of delta-sigma modulators using ZSIM , 1990, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[20]  É. Moulines,et al.  Convergence of a stochastic approximation version of the EM algorithm , 1999 .