针对存在不可忽略非应答的序贯结局的缺失模型选择

中文摘要 AI 辅助

纵向研究和多波调查中的序贯结局可能在早期和后期均存在非随机缺失。我们研究图形模型，其中至少一个结局是自删的，且后期结局的应答指标可能依赖于早期应答指标或已实现的早期结局。这些限制定义了两个候选族，在该族下相关的全数据分布可识别，且其观测数据模型存在重叠；同时包含两种依赖关系的图构成了超出预设比较范围的更广泛类别。对于每个候选族，我们在秩或完备性条件下建立全数据分布的可识别性，并开发基于似然的估计方法。随后，我们提出一种两阶段Vuong型程序：第一阶段确定候选模型是否可通过观测数据区分；仅在建立可区分性后，第二阶段才比较它们与真实观测数据分布的Kullback-Leibler散度。我们还证明，当所选模型具有固定为正的期望对数似然优势时，普通Wald推断对于所选模型特定的函数仍保持渐近有效性。模拟研究在各类图上评估了该两阶段程序，最后将该程序应用于比较Job Corps数据中的候选缺失模型，并在所选模型下执行下游函数估计。

英文摘要

Sequential outcomes in longitudinal studies and multi-wave surveys may be missing not at random at both earlier and later occasions. We study graphical models in which at least one outcome is self-censoring and the response indicator for a later outcome may depend on either the earlier response indicator or the realized earlier outcome. These restrictions define two candidate families under which the relevant full-data distributions are identifiable and whose observed-data models overlap; graphs containing both dependencies form a broader class outside the prespecified comparison. For each candidate family, we establish identification of the full-data distribution under rank or completeness conditions and develop likelihood-based estimation. We then propose a two-stage Vuong-type procedure. The first stage determines whether the candidate models are observationally distinguishable; only after distinguishability is established does the second stage compare their Kullback--Leibler divergences from the true observed-data distribution. We also show that ordinary Wald inference remains asymptotically valid for the selected model-specific functional when the selected model has a fixed positive expected log-likelihood advantage. Simulations evaluate the two-stage procedure across graph classes. We finally apply the procedure to compare candidate missingness models in the Job Corps data and perform downstream functional estimation under the selected model.

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35 pages, 3 figures