期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 11, 期 2, 页码 437-457出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186002760180608
关键词
EM algorithm; longitudinal data; Markov chain Monte Carlo; multiple imputation
This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Cur-rent methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as missing data, but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.
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