Window-sliding based NSP algorithm for multiple change-points estimation

A window-sliding based narrowest significance pursuit (WNSP) algorithm is proposed for multiple change-points estimation. The algorithm adopts a 'post-inference selection' approach: first, it automatically identifies the narrowest significant intervals containing at least one change point using the narrow significance tracking (NSP) method at a global significance level $ \alpha $; then, within each interval, it employs adaptive bandwidth and single-peak detection techniques to achieve precise estimation of change-point locations. Theoretical analysis confirms the method's consistency and finite-sample reliability under general noise conditions. Numerical simulations and real-world data analysis demonstrate the WNSP algorithm's effectiveness and robustness across diverse noise distributions and signal structures.