SEQUENTIAL IMPUTATIONS AND BAYESIAN MISSING DATA PROBLEMS
SEQUENTIAL IMPUTATIONS AND BAYESIAN MISSING DATA PROBLEMS
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DOI：
10.2307/2291224
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发表时间：
1994-03-01
影响因子：
3.7
通讯作者：
WONG, WH
中科院分区：
文献类型：
--
作者：
KONG, A;LIU, JS;WONG, WH
对于丢失数据的问题，Tanner和Wong描述了一种数据增强过程，该过程通过完全数据后验的混合来近似参数向量的实际后验分布。他们构建完整数据集的方法与吉布斯采样器密切相关。这两种算法都需要迭代，而且与EM算法类似，收敛速度可能很慢。在这篇文章中，我们介绍了一种替代程序，该程序包括按顺序输入缺失数据并计算适当的重要性抽样权重。在许多应用中，这一新过程无需迭代即可很好地工作。敏感度分析、影响分析。并且使用新数据进行更新可以以较低的成本执行。贝叶斯预测和模型选择也可以结合在一起。从广泛的应用中提取的例子被用来进行说明。
For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors. Their method of constructing the complete data sets is closely related to the Gibbs sampler. Both required iterations, and, similar to the EM algorithm, convergence can be slow. We introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights. In many applications this new procedure works very well without the need for iterations. Sensitivity analysis, influence analysis. and updating with new data can be performed cheaply. Bayesian prediction and model selection can also be incorporated. Examples taken from a wide range of applications are used for illustration.