Robust quickest correlation change detection in high-dimensional random vectors - Alghamdi, Assma; Banerjee, Taposh; Rajgopal, Jayant (Sequential analysis, 2025)
Detecting changes in high-dimensional vectors presents significant challenges, especially when the postchange distribution is unknown and time-varying. This article introduces a novel robust algorithm for correlation change detection in high-dimensional data. The algorithm is based on the maximum magnitude correlation coefficient and its approximate asymptotic density. The coefficient captures the level of correlation present in the data. The proposed approach is robust because it can help detect a change in correlation level from some known level to unknown, time-varying levels. The proposed test is also computationally efficient and valid for a broad class of data distributions. The effectiveness of the proposed algorithm is demonstrated on simulated data.