Bayesian Hierarchical Models for Ordinal and Missing Data

被引：0

作者：

Zhao Qiang ^{[1
]}

You Haiyan ^{[2
]}

机构：

[1] Shandong Normal Univ, Sch Math Sci, Jinan 250014, Shandong, Peoples R China

[2] Shandong Jianzhu Univ, Sch Sci, Jinan 250014, Shandong, Peoples R China

来源：

DATA PROCESSING AND QUANTITATIVE ECONOMY MODELING | 2010年

关键词：

Bayesian hierarchical models; Longitudinal data; Markov chain; Monte Carlo; Random effects;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

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.

引用

页码：464 / +

页数：2

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