@article{Zhang2025, 
author = {Xiaoyuan Zhang and Zhanshou Chen},
title = {Window-sliding based NSP algorithm for multiple change-points estimation},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {12},
pages = {29853-29872},
keywords = {change-points estimation, window-sliding, narrowest significance pursuit, confidence intervals, local statistic},
url = {https://www.sciopen.com/article/10.3934/math.20251311},
doi = {10.3934/math.20251311},
abstract = {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    α; 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.}
}