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.
Li et al. (Wed,) studied this question.
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