Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions
Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions
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DOI：
10.1214/17-sts630
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发表时间：
2018-05-01
影响因子：
5.7
通讯作者：
Daniels, Michael J.
中科院分区：
文献类型：
--
作者：
Linero, Antonio R.;Daniels, Michael J.
缺失数据几乎总是存在于真实的数据集中，并引入了一些统计问题。一个基本问题是，在没有强有力的不可检验的假设的情况下，感兴趣的影响通常不会被非参数化地识别。在这篇文章中，我们回顾了从基于可能性的角度使用识别限制的通用方法，并为最近提出的几种方法提供了联系点。这篇评论的一个重点是对非单调缺失的限制，一个在文献中被谨慎对待的主题。我们还提出了一个一般的，完全贝叶斯，方法是广泛适用的，能够处理各种识别的限制，以统一的方式。
Missing data is almost always present in real datasets, and introduces several statistical issues. One fundamental issue is that, in the absence of strong uncheckable assumptions, effects of interest are typically not non-parametrically identified. In this article, we review the generic approach of the use of identifying restrictions from a likelihood-based perspective, and provide points of contact for several recently proposed methods. An emphasis of this review is on restrictions for nonmonotone missingness, a subject that has been treated sparingly in the literature. We also present a general, fully Bayesian, approach which is widely applicable and capable of handling a variety of identifying restrictions in a uniform manner.