Section: New Results

Estimation in mixed-effects diffusion models

Participant : Marc Lavielle.

We have coupled the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models: we have considered some general mixed-effects diffusion models, in which observations are made at discrete time points and include measurement errors. In these models, the observed likelihood is generally not explicit, making maximum likelihood estimation of the parameters particularly complex. We have proposed a specific inference methodology for these models. In particular, it combines the SAEM algorithm with the extended Kalman filter to estimate the population parameters. We have also provided some tools for estimating the individual parameters, for recovering the individual underlying diffusion trajectories and for evaluating the model. We evaluated the methods on simulations and applied them to a pharmacokinetics example.