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.
Mots-clés : parameter estimation, SAEM algorithm, partial differential equations, KPP equation
@article{M2AN_2014__48_5_1303_0, author = {Grenier, E. and Louvet, V. and Vigneaux, P.}, title = {Parameter estimation in non-linear mixed effects models with {SAEM} algorithm: extension from {ODE} to {PDE}}, journal = {ESAIM: Mathematical Modelling and Numerical Analysis }, pages = {1303--1329}, publisher = {EDP-Sciences}, volume = {48}, number = {5}, year = {2014}, doi = {10.1051/m2an/2013140}, mrnumber = {3264355}, zbl = {1301.35177}, language = {en}, url = {http://www.numdam.org/articles/10.1051/m2an/2013140/} }
TY - JOUR AU - Grenier, E. AU - Louvet, V. AU - Vigneaux, P. TI - Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE JO - ESAIM: Mathematical Modelling and Numerical Analysis PY - 2014 SP - 1303 EP - 1329 VL - 48 IS - 5 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/m2an/2013140/ DO - 10.1051/m2an/2013140 LA - en ID - M2AN_2014__48_5_1303_0 ER -
%0 Journal Article %A Grenier, E. %A Louvet, V. %A Vigneaux, P. %T Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE %J ESAIM: Mathematical Modelling and Numerical Analysis %D 2014 %P 1303-1329 %V 48 %N 5 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/m2an/2013140/ %R 10.1051/m2an/2013140 %G en %F M2AN_2014__48_5_1303_0
Grenier, E.; Louvet, V.; Vigneaux, P. Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 48 (2014) no. 5, pp. 1303-1329. doi : 10.1051/m2an/2013140. http://www.numdam.org/articles/10.1051/m2an/2013140/
[1] Estimation techniques for distributed parameter systems, vol. 1. Systems & Control: Foundations & Appl. Birkhäuser Boston Inc., Boston, MA (1989). | MR | Zbl
and ,[2] Table-based modeling of delta-sigma modulators using Zsim. IEEE Trans. Computer-Aided Design Integr. Circuits Syst. 9 (1990) 142-150.
, , , and ,[3] Convergence of a stochastic approximation version of the EM algorithm. Ann. Statis. 27 94-128 (1999). | MR | Zbl
, and ,[4] Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations. Biometrics 66 (2010) 733-741. | MR | Zbl
, and ,[5] Parametric inference for mixed models defined by stochastic differential equations. ESAIM: PS 12 (2008) 196-218. | Numdam | MR | Zbl
and ,[6] The Genetical Theory of Natural Selection. Oxford University Press (1930). | JFM | MR
,[7] An introduction to the adjoint approach to design. Flow, Turbulence and Combustion 65 (2000) 393-415. | Zbl
and ,[8] CAzM: A circuit analyzer with macromodeling. IEEE Trans. Electron. Devices 30 (1983) 1207-1213.
, and ,[9] Inverse problems for partial differential equations, vol. 127. Appl. Math. Sci., 2nd edition. Springer, New York (2006). | MR | Zbl
,[10] Statistical and computational inverse problems, vol. 160. Appl. Math. Sci. Springer-Verlag, New York (2005). | MR | Zbl
and ,[11] Étude de l'equation de la diffusion avec croissance de la quantite de matiere et son application a un problème biologique. Bulletin de l'université d'État à Moscou, Section A I (1937) 1-26. | Zbl
, and ,[12] Maximum likelihood estimation in nonlinear mixed effects models. Comput. Statis. Data Anal. 49 (2005) 1020-1038. | MR
and ,[13] Private Communication (2012).
,[14] Population Approach & Mixed Effects Models - Models, Tasks, Tools & Methods. Avalaible at http://popix.lixoft.net/ INRIA (2013).
and ,[15] Estimation of population pharmacokinetic parameters of saquinavir in HIV patients with the monolix software. J. Pharmacokinetics and Pharmacodynamics 34 (2007) 229-249.
and ,[16] Optimal control of systems governed by partial differential equations. Translated from the French by S.K. Mitter. Springer-Verlag, New York (1971). | Zbl
,[17] Strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models. Metrika 63 (2006) 123-143. | Zbl
,[18] Strong consistency of mle in nonlinear mixed-effects models with large cluster size. Sankhya: Indian J. Statis. 67 (2005) 736-763. | Zbl
and ,[19] A Comprehensive Hepatitis C Viral Kinetic Model Explaining Cure. Clinical Pharmacology & Therapeutics 87 (2010) 706-713.
, , , , , , , , and ,[20] Inverse problem theory and methods for model parameter estimation. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2005). | MR | Zbl
,[21] The Monolix software, Version 4.1.2. Analysis of mixed effects models. Available at http://www.lixoft.com/ LIXOFT and INRIA (2012).
,[22] Efficient look-up-table-based modeling for robust design of sigma-delta ADCs. IEEE Trans. Circuits Syst. - I 54 (2007) 1513-1528.
and ,Cité par Sources :