论文标题
关于缺失数据模型中的可测试性和拟合测试的优点
On Testability and Goodness of Fit Tests in Missing Data Models
论文作者
论文摘要
在开发缺少数据问题的识别和估计技术方面取得了重大进展，在这些问题上可以通过有向的无环图描述建模假设。使用此类技术的结果的有效性依赖于图形保持True编码的假设；但是，对这些假设的验证在先前的工作中尚未得到足够的关注。在本文中，我们提供了有关三个丢失的数据图形模型的可测试含义的新见解，并为其设计拟合优度测试。所探索的模型类别是：顺序失踪 - 随机和丢失的非随机模型，可用于模拟使用辍学/审查的纵向研究，以及可以应用于横截面研究和调查的无自我审查模型。
Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rely on the assumptions encoded by the graph holding true; however, verification of these assumptions has not received sufficient attention in prior work. In this paper, we provide new insights on the testable implications of three broad classes of missing data graphical models, and design goodness-of-fit tests for them. The classes of models explored are: sequential missing-at-random and missing-not-at-random models which can be used for modeling longitudinal studies with dropout/censoring, and a no self-censoring model which can be applied to cross-sectional studies and surveys.
