Change-Point Detection and Its Modern Applications

We review recent advances in change-point detection methods across three important fields of statistics: (a) We first present a subgroup identification method based on a multi-threshold change plane model where the subgroup boundaries are defined by a high-dimensional hyperplane in the covariate space. Subjects grouped into different regions may receive more individualized treatments in medical research studies and achieve improved health outcomes. (b) We then consider the estimation of discontinuity for functional process data. Many longitudinal or functional responses may exhibit abrupt jumps, and our methodology effectively accommodates such complicated nonsmooth features. (c) Finally, we explore change-point estimation within dynamic networks using a recently proposed network autoregressive model. This framework demonstrates that community structures in networks can shift similarly to changes observed in time series data. These reviews highlight the wide-ranging applications of change-point detection methodologies in modern data analysis.