标题： 时变关系计数数据的双聚类分析 显示英文标题

标题： Bi-clustering for time-varying relational count data analysis

摘要： 关系计数数据通常来自在线商店的同步购买和社交网络服务信息等来源。对这种关系计数数据进行双聚类可以揭示对象（如家庭用品或人）之间关系的潜在结构。当在多个时间点观察到的关系计数数据可用时，值得将时间结构纳入双聚类结果中，以了解对象如何随着时间在聚类之间移动。 本文提出了两种用于分析时变关系计数数据的双聚类方法。第一种模型称为动态泊松无限关系模型（dPIRM），用于处理时变关系计数数据。在第二种模型中，我们称之为动态零膨胀泊松无限关系模型，我们进一步扩展了dPIRM，使其能够处理零膨胀数据。提出这两种模型非常重要，因为零膨胀数据经常出现，尤其是在时间间隔较短的情况下。此外，通过明确推导相关的完全条件分布，我们描述了估计参数的特征，并由此得出两种模型之间的关系。我们通过模拟研究和真实数据实例展示了这两种模型的有效性。 显示英文摘要

摘要： Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. Bi-clustering such relational count data reveals the latent structure of the relationship between objects such as household items or people. When relational count data observed at multiple time points are available, it is worthwhile incorporating the time structure into the bi-clustering result to understand how objects move between the cluster over time. In this paper, we propose two bi-clustering methods for analyzing time-varying relational count data. The first model, the dynamic Poisson infinite relational model (dPIRM), handles time-varying relational count data. In the second model, which we call the dynamic zero-inflated Poisson infinite relational model, we further extend the dPIRM so that it can handle zero-inflated data. Proposing both two models is important as zero-inflated data are often encountered, especially when the time intervals are short. In addition, by explicitly deriving the relevant full conditional distributions, we describe the features of the estimated parameters and, in turn, the relationship between the two models. We show the effectiveness of both models through a simulation study and a real data example.