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Activity Number: 263
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #319637
Title: Statistical Inference on Dynamic System Models with Multiple Observation Units
Author(s): Hongyuan Wang* and David Allen
Companies: and University of Kentucky
Keywords: Dynamic System ; SAEM algorithm ; Mixed Model
Abstract:

In this poster we would like to present an integrated scheme of statistical inference on dynamical system models, with emphasis on a set of nonlinear ordinary differential equations with possibly no analytic solutions and multiple observation units. We combined the differential equation model with mixed effect model to characterize the typical parameter values in the population and the extent of their variation. The estimation methodology includes implementation of Stochastic Approximation Expectation Maximization (SAEM) algorithm with numerical ODE solvers, derivative-free optimization algorithms, such as differential evolution, and parallel Markov chain Monte Carlo (MCMC) algorithms. Both maximum likelihood and restricted maximum likelihood estimation are derived. The validity of the inference is based on detailed analysis of simulation and case studies.


Authors who are presenting talks have a * after their name.

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