Title: Bayesian inference on multivariate-t nonlinear mixed-effects models for multiple longitudinal data
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Luis Mauricio Castro - Pontificia Universidad Catolica de Chile (Chile)
Abstract: The multivariate-t nonlinear mixed-effects model (MtNLMM) has been shown a promising robust tool for analyzing multiple longitudinal trajectories following arbitrary growth patterns in the presence of outliers and possible missing responses. Owing to intractable likelihood function of the model, a fully Bayesian estimating procedure is presented to account for the uncertainties of model parameters, random effects, and missing responses via the Markov chain Monte Carlo method. Posterior predictive inferences for the future values are also investigated. A simulation study is conducted to demonstrate the feasibility of our Bayesian sampling schemes. The proposed techniques are illustrated through applications to case studies.