Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event. We demonstrate the utility of the model by focusing on a dataset that defines ‘one-sided-killing’ as government-caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths for each country-year in the dataset. These new event-based, count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time. Our modeling framework also provides a principled method for inferring unobserved count variables based on conceptually related categorical information.

中文翻译：

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从稳健的人权衡量模型估计单方面杀戮

统计镇压事件很困难，因为国家领导人有隐瞒下属行为和销毁虐待证据的动机。在本文中，我们扩展了抑制研究中现有的潜在变量建模技术，以解释为此类难以观察的事件生成的计数数据中固有的不确定性。我们通过关注将“单方面杀戮”定义为政府造成的非战斗人员死亡的数据集来证明该模型的实用性。除了对潜在抑制水平进行更精确的估计外，该模型还估计了一个国家从事单方面杀戮的概率以及数据集中每个国家/年的死亡预测分布。这些新的基于事件的，计数估计对于对此类数据感兴趣但对此类事件在不同国家和时间段内的可比性持怀疑态度的研究人员很有用。我们的建模框架还提供了一种基于概念相关的分类信息推断未观察到的计数变量的原则方法。