A Distribution-Free Multivariate Control Chart
Nan Chen
Changliang Zou
Xuemin Zi
10.6084/m9.figshare.1423385.v1
https://tandf.figshare.com/articles/A_Distribution_Free_Multivariate_Control_Chart/1423385
<div><p>Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This paper develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations on-line rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.</p></div>
2015-05-16 00:00:00
nonparametric methodology
process control
studies show
Supplementary materials
reference dataset
distribution functions
control chart
time point
alarm rate
process distribution
multivariate SPC
control limit
multivariate process shifts
data streams
monitoring location parameters
charting statistic