A sequential feature selection approach to change point detection in mean-shift change point models

Abstract

Change point detection is an important area of scientific research and has applications in a wide range of fields. In this paper, we propose a sequential change point detection (SCPD) procedure for mean-shift change point models. Unlike classical feature selection based approaches, the SCPD method detects change points in the order of the conditional change sizes and makes full use of the identified change points information. The extended Bayesian information criterion (EBIC) is employed as the stopping rule in the SCPD procedure. We investigate the theoretical property of the procedure and compare its performance with other methods existing in the literature. It is established that the SCPD procedure has the property of detection consistency. Simulation studies and real data analyses demonstrate that the SCPD procedure has the edge over the other methods in terms of detection accuracy and robustness.