Phase II monitoring of generalized linear profiles under different types of changes

Abstract

Various control charts have been proposed to monitor generalized linear pro les in Phase II. However, the robustness of the proposed methods in detecting di erent types and especially di erent directions of changes is not well-studied in the literature. In real-world applications, di erent kinds of change such as drift and multiple changes are likely to occur, which can be isotonic (increasing) or antitonic (decreasing). This paper studies the robustness of the Rao Score Test (RST) method, T2, and Multivariate Exponential Weighted Moving Average (MEWMA) in di erent types, drift and multiple, and directions of changes. The RST method also bene ts from a change-point detection approach whose performance is studied as well. According to the results, generally, the RST method shows a better performance in detecting di erent types of changes. Moreover, the performance of the RST method is robust to the direction of the change, while T2 and MEWMA are not ARL-unbiased and show di erent performances under isotonic and antitonic changes. Therefore, to address this issue, a bias-reduced estimator is proposed for use in T2. The results demonstrate that the proposed control chart outperforms T2 and is less biased than T2. Finally, a real-world problem is presented in which the aforementioned methods are applied to real data.