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Bayesian estimation of simplex distribution nonlinear mixed models for longitudinal data

Yuan-Ying Zhao, Deng-Ke Xu, Xing-De Duan, Liang Dai


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.


Bayesian analysis; Gibbs sampler; Longitudinal data; Metropolis-Hastings algorithm;Simplex distribution nonlinear mixed models.

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