Title: Using random partition models for flexible change-point analysis in multivariate processes
Authors: Garritt Page - BYU (United States) [presenting]
Abstract: 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 individual processes. A method is described 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, the approach is based on developing a dependent model for these types of partitions. We describe computational strategies and illustrate improved performance in detecting change points through a small simulation study. We then apply the method to a financial data set of emerging markets in Latin America.