Change-point detection based on Wilcoxon type U-statistic and block wild bootstrap

This paper considers U-statistics with the Block Wild Bootstrap (BWB) to detect change points in the mean of time series.While the conventional CUSUM test is widely used, its performance deteriorates in the presence of autocorrelation, non-normality or outliers, often resulting in substantial size distortion.In contrast, the proposed BWB Wilcoxon type U-statistic test is more robust to non-normality and outliers.It maintains stable size control and high statistical power across various challenging settings, including those with strong autocorrelation and extreme observations.In particular, when strong outliers are present, the BWB procedure alleviates the conservative tendency of the classical U-statistic and substantially improves its finite-sample performance.This study offers a practical alternative to existing methods for detecting change points in time series with complex dependence structures or data irregularities.