In recent years, the monitoring of compositional data using control charts has been investigated in Statistical Process Control field. this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) chart with variable sampling intervals to monitor based on isometric log-ratio transformation. The Time Signal be computed Markov chain approach investigate performa...

We derive several multivariate control charts to monitor the mean vector of multivariate GARCH processes under the presence of changes, by means of maximizing the generalized likelihood ratio. This presentation is rounded up by a comparative performance study based on extensive Monte Carlo simulations. An empirical illustration shows how the obtained results can be applied to real data. Note: T...

this paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. we extend the use o...

Monitoring multivariate quality characteristics is very common in production and service environment. Therefore, many control charts have been suggested by authors for monitoring multivariate processes. In another side, profile monitoring is a new approach in the area of statistical process control. In this approach, the quality of a product or a process is characterized by a relation between o...

In this paper, a brief outline of the evolution of the statistical techniques for the measurement of quality is firstly sketched. At the same time, the need for multivariate procedures is justified from both methodological and application points of view and a proposal of non parametric control charts is shown. The attention is then focused on visualising and monitoring quality. The peculiar fea...