期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 52, 期 7, 页码 2899-2923出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2021.1921799
关键词
Bayesian; Gibbs sampling; Missing data; Multilevel modeling; Multiple imputation
This study provides a coherent approach to Bayesian analysis of multilevel models in the presence of missing data, covering the main aspects of the models and missingness.
Missing data are a common occurrence in analyses of multivariate data, including in multilevel modeling. Bayesian approaches to handling missing data in multilevel modeling have garnered increasing attention, either on their own or in service of multiple imputation. However, these applications are largely confined to specific models or missingness patterns. The current work provides a coherent account of Bayesian analysis of multilevel models in the presence of missing data on the outcomes, level-1 predictors, and level-2 predictors, that covers the main aspects of the models and missingness. In doing so, this work provides a grounding for estimation in fully Bayesian approaches that employ Gibbs sampling, and provides an account of how to generate the imputations in the first phase of a multiple imputation approach.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据