The aim of online monitoring is to issue an alarm as soon as there is significant evidence in the collected observations to suggest that the underlying data generating mechanism has changed. This work is concerned with open-end, nonparametric procedures that can be interpreted as statistical tests. The proposed monitoring schemes consist of computing the so-called retrospective CUSUM statistic (or minor variations thereof) after the arrival of each new observation. After proposing suitable threshold functions for the chosen detectors, the asymptotic validity of the procedures is investigated in the special case of monitoring for changes in the mean, both under the null hypothesis of stationarity and relevant alternatives. To carry out the sequential tests in practice, an approach based on an asymptotic regression model is used to estimate high quantiles of relevant limiting distributions. Monte Carlo experiments demonstrate the good finite-sample behavior of the proposed monitoring schemes and suggest that they are superior to existing competitors as long as changes do not occur at the very beginning of the monitoring. Extensions to statistics exhibiting an asymptotic mean-like behavior are briefly discussed. Finally, the application of the derived sequential change-point detection tests is succinctly illustrated on temperature anomaly data.
Mark Holmes is supported by Future Fellowship FT160100166 from the Australian Research Council.
The authors would like to thank Ed Perkins for helpful discussions as well as Josua Gösmann and an anonymous Referee for their constructive comments on an earlier version of this manuscript.
"Open-end nonparametric sequential change-point detection based on the retrospective CUSUM statistic." Electron. J. Statist. 15 (1) 2288 - 2335, 2021. https://doi.org/10.1214/21-EJS1840