A Distribution-free Multivariate Change-point Model for Statistical Process Control |
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Authors: | Maoyuan Zhou Xuemin Zi Wei Geng |
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Affiliation: | 1. College of Science, Civil Aviation University of China, Tianjin, P. R. China;2. LPMC and Institute of Statistics, Nankai University, Tianjin, P. R. China;3. School of Science, Tianjin University of Technology and Education, Tianjin, P. R. China;4. LPMC and Institute of Statistics, Nankai University, Tianjin, P. R. China |
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Abstract: | This article develops a new distribution-free multivariate procedure for statistical process control based on minimal spanning tree (MST), which integrates a multivariate two-sample goodness-of-fit (GOF) test based on MST and change-point model. Simulation results show that our proposed procedure is quite robust to nonnormally distributed data, and moreover, it is efficient in detecting process shifts, especially moderate to large shifts, which is one of the main drawbacks of most distribution-free procedures in the literature. The proposed procedure is particularly useful in start-up situations. Comparison results and a real data example show that our proposed procedure has great potential for application. |
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Keywords: | Change-point Minimal spanning tree Multivariate goodness-of-fit test Multivariate statistical process control |
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