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In a sequence of multivariate observations or non-Euclidean data objects,such as networks, local dependence is common and could lead to falsechange-point discoveries. We propose a new way of permutation -- circular blockpermutation with a random starting point -- to address this problem. Thispermutation scheme is studied on a non-parametric change-point detectionframework based on a similarity graph constructed on the observations, leadingto a general framework for change-point detection for data with localdependency. Simulation studies show that this new framework retains the samelevel of power when there is no local dependency, while it controls type Ierror correctly for sequences with and without local dependency. We also derivean analytic p-value approximation under this new framework. The approximationworks well for sequences with length in hundreds and above, making thisapproach fast-applicable for long data sequences.

Change-point detection for multivariate and non-Euclidean data with local dependency