Change-Point Detection for Multivariate and Non-Euclidean Data with Local Dependence

Thomas M. Menino Convention & Exhibition Center 

In sequences comprising multivariate observations or non-Euclidean data objects such as networks, local dependencies often arise and can lead to inaccurate change-point detection. We propose a versatile framework that accommodates such sequences without imposing distributional assumptions on the observations, while being applicable to both high-dimensional and non-Euclidean data. This framework provides closed-form expressions for the test statistic and for approximating the p-value, allowing for efficient application in practice. Because it is often unclear whether a sequence exhibits local dependency in real applications, we further introduce a data-driven criterion to guide implementation of the proposed approach.