Joint Random Partition Models for Multivariate Change Point Analysis

Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underly-ing 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 infor-mation. 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 ap-proach 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.