Bayesian estimation of simplex distribution nonlinear mixed models for longitudinal data

The main thrust of this paper is to study Bayesian analysis of simplex distribution nonlinear mixed models for longitudinal data. A hybrid algorithm that combines the Gibbs sampler and Metropolis-Hastings algorithm is implemented to produce the joint Bayesian estimates of parameters and random effects. Simulation studies and a real example are presented to illustrate the methodologies.