Control charts for high-dimensional time series with estimated in-control parameters

• Published in: Sequential analysis Vol. 43; no. 1; pp. 103 - 129
• Main Authors: Bodnar, Rostyslav, Bodnar, Taras, Schmid, Wolfgang
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.01.2024; Taylor & Francis Ltd
• Subjects: Control charts; Empirical analysis; Euclidean geometry; High-dimensional time series; Mathematical models; MEWMA control chart; Parameters; sequential surveillance; Statistics; vector autoregressive process
• ISSN: 0747-4946; 1532-4176; 1532-4176
• DOI: 10.1080/07474946.2023.2288135

In this article, we study the effect of misspecification caused by fitting the target process in the Phase I analysis of the monitoring procedure on the behavior of several types of multivariate exponentially weighted moving average (MEWMA) control charts in the high-dimensional setting. In particular, the classical MEWMA control charts, whose control statistics are based on the exact and asymptotic Mahalanobis distance, are considered together with the novel approaches where the Euclidean distance and the diagonalized Euclidean distance are employed in the construction of control statistics. The high-dimensional distributions of the control statistics are deduced at each time. These results are later used to assess the performance of the considered control charts under misspecification. Both theoretical and empirical findings lead to the conclusion that the control charts based on the Euclidean distance and the diagonalized Euclidean distance are robust to misspecification for moderate dimensions of the data-generating model, whereas they tend to overestimate the in-control average run lengths (ARLs) in the case of larger dimensions. On the other hand, the control schemes based on the Mahalanobis distance are considerably affected by the estimation of the parameters of the target process, and their application results in drastically smaller values of the ARLs, especially when the dimension of the data-generating model is large.