论文标题
使用联合随机分区模型在多元过程中进行灵活的变更点分析
Using Joint Random Partition Models for Flexible Change Point Analysis in Multivariate Processes
论文作者
论文摘要
变更点分析涉及确定有序随机过程的位置，该过程经历了某些基本分布的突然变化。当观察到多个过程时，通常会在不同过程中共享有关变更点位置的信息。这项工作描述了一种利用此类信息的方法。由于可以通过与连续簇的分区来描述变化点的数量和位置，因此我们的方法为这些类型的分区开发了联合模型。我们描述了与我们的方法相关的计算策略，并通过一项小型模拟研究说明了检测变化点的性能的提高。然后，我们将方法应用于拉丁美洲新兴市场的财务数据集，并突出了由于这些经济体之间的变化点位置之间的相关性而发现的有趣见解。
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.
