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 …

Memory control chart such as multivariate CUSUM (MCUSUM) and multivariate EWMA
(MEWMA) control charts are considered superior for the detection of small-to-moderate …

The manuscript addresses the copula-based CUSUM charting scheme to monitor infectious
disease. To facilitate the disease surveillance, the Poisson distribution is often used to …

The number of event occurrences, called counts are prevalent in many fields such as
manufacturing industry and public health. Control charts have been widely employed to …

Statistical process control for count data has attracted increasing attention in recent years.
The need for efficient control charts suitable for autocorrelated multivariate count processes …

One of the most widely used multivariate control charts is the Hotelling T2. In order to
construct a Hotelling T2 control chart, the mean vector (μ) and the variance–covariance …

The fraction nonconforming, which follows a binomial distribution, is one of the most critical
quality characteristics of attribute processes. In addition, the multivariate binomial process …

High-quality processes, characterized by low defect rates, typically exhibit an exponential
distribution for time-between-events (TBE) data. To effectively monitor TBE data, one-sided …

We consider the monitoring of multivariate correlated count data, which have many
applications in practice. Although there are quite a few methods for the statistical process …

Among attribute processes, the number of nonconformities conforming to a Poisson
distribution is among the most crucial quality attributes. Furthermore, owing to the variety of …