Top PDF Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE
Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE
... Abstract: Parameter estimation in non linear mixed effects models requires a large number of evaluations of the model to ...consuming to evaluate it ...
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Performance in population models for count data, part II: a new SAEM algorithm.
... data from clinical trials using mixed effect analysis has recently become widely ...the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain ...
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Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
... recent estimation algorithms, SAEM, in R, to obtain maximum likelihood ...alternative to linearisation-based estimation methods as implemented for instance in nlme and ...
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Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model
... exact SAEM algorithm could also be extended to this case but is out of the scope of this ...paper. To take into account the censored-data problem with NLMEM, Wu (7; 17) proposes MCEM ...
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The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model.
... Abstract Non-linear mixed-effects models (NLMEMs) are used to improve information gather- ing from longitudinal studies and applied to treatment evaluation ...
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saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect models
... simulation in lini
al drug development is now well ...data, with ea
h subje
t providing several measurements throughout the ...staple in parti
ular of pharma
okineti (PK) and pharma
odynami (PD) ...
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EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models
... Keywords. Mixed models, Stochastic Differential Equations, SAEM algorithm, Particle Filter, PMCMC, Stochastic volatility, Time-inhomogeneous SDE ...used to discriminate between the ...
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Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.
... variability in two-levels NLMEMs. They show that neglecting it resulted in biased estimates for the fixed ...The parameter estimation of NLMEMs is not trivial because the likelihood of NLMEMs ...
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Bivariate linear mixed models using SAS proc MIXED.
... methods to fit evolution of CD4 and CD8 cells [4] or CD4 and 2 microglobuline ...EM algorithm to fit a bivariate linear random effects ...method to fit a bivariate ...
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A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences
... observed in images are related to the physical properties of the object, as illustrated on Figure 2 where each kind of tissues has a specific gray-level ...Due to acquisition noise, the image is ...
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PDE Models in Macroeconomics
... everyone in the economy falls by ten ...jumps from state 1 to state 2 with intensity φ 1 and vice versa with intensity φ 2 ...difficulty: in contrast to the case without ...
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BIC selection procedures in mixed effects models
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ...
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Delay-robust stabilization of a hyperbolic PDE–ODE system
... the in-domain couplings present in the PDEs and possibly attenuating the reflections on the controlled side (depending on the choice of a tun- ing ...these in-domain couplings, the new system can be ...
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Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects
... the estimation of Markov (and semi-Markov) switching linear mixed models ...i.e. models that combine linear mixed models with individual- wise random ...
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Robust control design of underactuated 2 × 2 PDE-ODE-PDE systems
... of in- domain coupling and of the ODE), it can easily be seen that the exponential stability of the states u 1 (t, 0), v 2 (t, 1) is equivalent to the exponential stability of the whole ...Thus, ...
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Parameter estimation for energy balance models with memory
... everywhere in the PDE’s domain of definition D, using only temperature infor- mation in a small subdomain E ⊂ ...correspond to exact measurements of the ...model parameter of the EBMM using ...
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Linear inverse problems with various noise models and mixed regularizations
... case. In the context of Poisson linear inverse problems using sparsity-promoting regularization, a few recent algorithms have been ...Rachford) to solve the corresponding optimization problem. The ...
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Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion
... performed with an extended version of the framework described in [12]: the main algorithm is the CMA-ES genetic algorithm, which asks at each iteration for the score of a high number of 3D ...
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A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.
... dom effects and residuals, either in a nonparametric or in a parametric ...random effects into the classical resid- ual bootstrap plays therefore a very important role for bootstrapping ...
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Modelling finger force produced from different tasks using linear mixed models with lme R function
... order to provide answers to study objectives, we introduce two contrast ...presented in Table 14. Extract of results are displayed in Tables 15 and ...corresponding to the number of ...
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