Paper Info
Title
An EWMA control chart for the multivariate coefficient of variation.
Abstract
Monitoring the multivariate coefficient of variation over time is a natural choice when the focus is on stabilising the relative variability of a multivariate process, as is the case in a significant number of real situations in engineering, health sciences, and finance, to name but a few areas. However, not many tools are available to practitioners with this aim. This paper introduces a new control chart to monitor the multivariate coefficient of variation through an exponentially weighted moving average (EWMA) scheme. Concrete methodologies to calculate the limits and evaluate the performance of the chart proposed and determine the optimal values of the chart's parameters are derived based on a theoretical study of the statistic being monitored. Computational experiments reveal that our proposal clearly outperforms existing alternatives, in terms of the average run length to detect an out-of-control state. A numerical example is included to show the efficiency of our chart when operating in practice.
Year
DOI
Venue
2019
10.1002/qre.2459
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
Keywords
DocType
Volume
average run length,doubly noncentral F distribution,EWMA,multivariate coefficient of variation,Nelder-Mead method,trimmed mean
Journal
35.0
Issue
ISSN
Citations 
6.0
0748-8017
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Vicent Giner-Bosch100.34
Kim Phuc Tran201.01
Philippe Castagliola352961.65
Michael B. C. Khoo428249.97