Pattern graphs: A graphical approach to nonmonotone missing data
Pattern graphs: A graphical approach to nonmonotone missing data
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DOI：
10.1214/21-aos2094
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发表时间：
2020-04
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影响因子：
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通讯作者：
Yen-Chi Chen
中科院分区：
文献类型：
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作者：
Yen-Chi Chen
我们引入了模式图的概念——表示响应模式如何关联的有向无环图。模式图表示非参数识别/饱和的识别限制，通常是缺失的非随机限制。我们利用模式图引入了选择模型和模式混合模型的表述，并证明了它们是等价的。模式图导致一个逆概率加权估计器以及一个基于假设的估计器。研究了这两个估计量的渐近理论，并给出了计算这两个估计量的基于图的递归方法。提出了三种基于图的灵敏度分析方法，并研究了模式图的等价类。
We introduce the concept of pattern graphs--directed acyclic graphs representing how response patterns are associated. A pattern graph represents an identifying restriction that is nonparametrically identified/saturated and is often a missing not at random restriction. We introduce a selection model and a pattern mixture model formulations using the pattern graphs and show that they are equivalent. A pattern graph leads to an inverse probability weighting estimator as well as an imputation-based estimator. Asymptotic theories of the estimators are studied and we provide a graph-based recursive procedure for computing both estimators. We propose three graph-based sensitivity analyses and study the equivalence class of pattern graphs.