Bayesian Hierarchical Models for Ordinal and Missing Data

摘要

Longitudinal data arise if outcomes are measured repeatedly following time. Bayesian hierarchical models have been proved to be a powerful tool for analysis of longitudinal data with computation being performed by Markov chain Monte Carlo (MCMC) methods. The hierarchical models extend the random effects models by including a prior on the regression coefficients and parameters in the distribution of the random effects. The WinBUGS project can be utilized for the computation of MCMC.