基于树图和共轭几率的多元缺失建模
Modeling Multivariate Missingness with Tree Graphs and Conjugate Odds
AI总结：
本文提出基于树图和共轭几率族的多元缺失建模方法，通过构建共轭模型和简单填补模型，实现对完整数据分布的优雅建模，并通过模拟和真实数据验证其有效性。
摘要：
本文分析了一种特定类型的缺失不是随机（MNAR）假设，称为树图，扩展了模式图的工作。我们在先前的工作基础上引入了共轭几率族的概念，其中某些参数模型在选择几率上可以保持数据分布族在所有缺失数据模式下。在共轭几率族和树图假设下，我们能够优雅地建模完整数据分布，即对于观测数据，我们获得一个与完整数据共轭的模型，而对于缺失条目，我们创建了一个简单的填补模型。此外，我们还研究了图选择、敏感性分析和统计推断问题。通过模拟和真实数据，我们展示了我们方法的适用性。
英文摘要：
In this paper, we analyze a specific class of missing not at random (MNAR) assumptions called tree graphs, extending upon the work of pattern graphs. We build off previous work by introducing the idea of a conjugate odds family in which certain parametric models on the selection odds can preserve the data distribution family across all missing data patterns. Under a conjugate odds family and a tree graph assumption, we are able to model the full data distribution elegantly in the sense that for the observed data, we obtain a model that is conjugate from the complete-data, and for the missing entries, we create a simple imputation model. In addition, we investigate the problem of graph selection, sensitivity analysis, and statistical inference. Using both simulations and real data, we illustrate the applicability of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
