We review methods for monitoring multivariate time-between-events (TBE) data. We present
some underlying complexities that have been overlooked in the literature. It is helpful to …

In this paper, we develop two adaptive approaches for detecting the signal source in a
bivariate process when a shift occurs in the location vector or the scale matrix or both. The …

To improve the detecting abilities of upward and downward parameter changes in high‐
quality process, two combined schemes by integrating memory‐type, that is, exponentially …

The purpose of this research was to prepare silver nanoparticles in a tubular reactor using
tannic acid as both a reducing agent and a stabiliser. The stability of the process was …

Monitoring the vector of times between multiple events is essential in a high-quality process
such as healthcare operations. To this end, the multivariate time between events (TBE) …

The traditional approach of statistical process control is to estimate the in-control parameters
from reference samples obtained in Phase I and then apply them to Phase II monitoring …

In recent years, several techniques based on control charts have been developed for the
simultaneous monitoring of the time interval T and the amplitude X of events, known as time …

The copula approach is a popular method for multivariate modeling applied in several fields;
it defines non-parametric measures of dependence between random variables. In this …

In recent years, several control charts have been developed for the simultaneous monitoring
of the time interval T and the amplitude X of events, denoted as the TBEA (Time Between …

A multivariate exponentially weighted moving average (MEWMA) and Hotelling's T2 control
charts are types of multivariate control charts for monitoring the mean vector. In this paper …