  We propose a new framework for the detection of change-points in online,
sequential data analysis. The approach utilizes nearest neighbor information
and can be applied to sequences of multivariate observations or non-Euclidean
data objects, such as network data. Different stopping rules are explored, and
one specific rule is recommended due to its desirable properties. An accurate
analytic approximation of the average run length is derived for the recommended
rule, making it an easy off-the-shelf approach for real multivariate/object
sequential data monitoring applications. Simulations reveal that the new
approach has better performance than likelihood-based approaches for high
dimensional data. The new approach is illustrated through a real dataset in
detecting global structural changes in social networks.
