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We propose two procedures to detect a change in the mean of high-dimensionalonline data. One is based on a max-type U-statistic and another is based on asum-type U-statistic. Theoretical properties of the two procedures are exploredin the high dimensional setting. More precisely, we derive their average runlengths (ARLs) when there is no change point, and expected detection delays(EDDs) when there is a change point. Accuracy of the theoretical results isconfirmed by simulation studies. The practical use of the proposed proceduresis demonstrated by detecting an abrupt change in PM2.5 concentrations. Thecurrent study attempts to extend the results of the CUSUM and Shiryayev-Robertsprocedures previously established in the univariate setting.

Max-Type and Sum-Type Procedures for Online Change-Point Detection in the Mean of High-Dimensional Data