Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.

翻译：变化点分析涉及确定一个有秩序的随机过程的位置,该过程在某些基本分布中发生突然的局部变化。当观测到多个过程时,往往会发现关于变化点位置的信息在不同过程之间共享。这项工作描述了一种利用这类信息的方法。由于变化点的数量和位置可以通过与毗连的集群的分割来描述,我们的方法为这些类型的分割区开发了一个联合模型。我们描述了与我们的方法相关的计算战略,并用一个小型模拟研究来说明在探测变化点方面的改进。我们然后将我们的方法应用于拉丁美洲新兴市场的一套金融数据,并突出由于这些经济体中的变化点位置之间的关联而发现的有趣的洞见。