Abstract
AbstractControl chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.
Publisher
Springer Science and Business Media LLC
Reference23 articles.
1. Das, S. et al. Identifica- tion of hot and cold spots in genome of Mycobacterium tuberculosis using Shewhart control charts. Scientific Reports. 2, 297–297 (2012).
2. Hotelling, H. Multivariate quality control–illustrated by air testing of sample bombsights. In: Eisenhart, C., Hastay, M.W. and Wallis, W.A., Eds., Techniques of Statistical Analysis, McGraw Hill, New York. 111–184 (1947).
3. Lowry, C. A., Woodall, W. H., Champ, C. W. & Rigdon, S. E. A multivariate exponentially weighted moving average control chart. Technometrics. 34, 46–53 (1992).
4. Sullivan, J. H. & Woodall, W. H. Change–point detection of mean vector or covariance matrix shifts using multivariate individual observations. IIE Transactions. 32, 537–549 (2000).
5. Yue, J. & Liu, L. Multivariate nonparametric control chart with variable sampling interval. Applied Mathematical Modelling. 52, 603–612 (2017).
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