Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data. We propose a co-clustering model, based on the binary Latent Block Model, that aims to take advantage of this nonignorable nonresponses, also known as Missing Not At Random data. A variational expectation–maximization algorithm is derived to perform inference and a model selection criterion is presented. We assess the proposed approach on a simulation study, before using our model on the voting records from the lower house of the French Parliament, where our analysis brings out relevant groups of MPs and texts, together with a sensible interpretation of the behavior of non-voters.

缺失的数据可以提供信息。当数据模型不允许从缺失的数据中提取信息时，忽略这些信息可能会导致误导性的结论。我们提出了一种基于二元潜在块模型的协同聚类模型，旨在利用这种不可忽略的非响应，也称为缺失非随机数据。导出了一种变分期望最大化算法来执行推理，并提出了模型选择标准。在对法国议会下议院的投票记录使用我们的模型之前，我们在模拟研究中评估了提议的方法，我们的分析得出了相关的国会议员和文本群体，以及对非议会议员行为的合理解释。选民。