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.

关键词： bayesian hierarchical models longitudinal data markov chain monte carlo random effects

作者: ZHAO Qiang YOU Haiyan

作者单位: School of Mathematical Sciences, Shandong Normal University, P.R.China, 250014 School of Science, Shandong Jianzhu University, P.R.China, 250014

会议类型: 国际会议

会议名称: The 3rd International Institute of Statistics & Management Engineering Symposium(2010 国际统计与管理工程研讨会 IISMES)

会议地点: 威海

会议语种:英文

页码: 464-467

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）