Parametric and nonparametric multivariate control charts that are proven very useful in monitoring the covariance matrix of multivariate normally or “nearly” normally distributed continuous datasets have been proposed in statistical process control (SPC) literature. However, in many recent practical applications of SPC, the underlying systems or processes are characterised by discrete or a mixture of discrete and continuous multivariate random variables. In such cases, the available multivariate control charts for monitoring the covariance matrix of continuous processes are inadequate. We propose a multivariate nonparametric Shewhart-type chart for monitoring shifts in the covariance matrix of multivariate discrete or mixture of discrete an...
Autocorrelated data are common in today's process control applications. Many of these applications i...
In this article, we present a new control chart for monitoring the covariance matrix in a bivariate ...
The multivariate and discrete data are commonly used to monitor product defects and epidemic disease...
Multivariate control charts are generally used in industries for monitoring and diagnosing processes...
Multivariate control charts are essential tools in multivariate statistical process control. In real...
Multivariate control charts are essential tools in multivariate statistical process control. In real...
[[abstract]]Most of the existing control charts for monitoring multivariate process variability are ...
Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of...
[[abstract]]A control chart is proposed to effectively monitor changes in the population variance-co...
<div><p>Monitoring multivariate quality variables or data streams remains an important and challengi...
In this article, we present a new control chart for monitoring the covariance matrix in a bivariate ...
In this article, we propose a control chart for detecting shifts in the covariance matrix of a multi...
Monitoring multivariate and high-dimensional data streams is often an essential requirement for qual...
Multivariate monitoring techniques such as multivariate control charts are used to control the proce...
In this article we consider a control chart based on the sample variances of two quality characteris...
Autocorrelated data are common in today's process control applications. Many of these applications i...
In this article, we present a new control chart for monitoring the covariance matrix in a bivariate ...
The multivariate and discrete data are commonly used to monitor product defects and epidemic disease...
Multivariate control charts are generally used in industries for monitoring and diagnosing processes...
Multivariate control charts are essential tools in multivariate statistical process control. In real...
Multivariate control charts are essential tools in multivariate statistical process control. In real...
[[abstract]]Most of the existing control charts for monitoring multivariate process variability are ...
Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of...
[[abstract]]A control chart is proposed to effectively monitor changes in the population variance-co...
<div><p>Monitoring multivariate quality variables or data streams remains an important and challengi...
In this article, we present a new control chart for monitoring the covariance matrix in a bivariate ...
In this article, we propose a control chart for detecting shifts in the covariance matrix of a multi...
Monitoring multivariate and high-dimensional data streams is often an essential requirement for qual...
Multivariate monitoring techniques such as multivariate control charts are used to control the proce...
In this article we consider a control chart based on the sample variances of two quality characteris...
Autocorrelated data are common in today's process control applications. Many of these applications i...
In this article, we present a new control chart for monitoring the covariance matrix in a bivariate ...
The multivariate and discrete data are commonly used to monitor product defects and epidemic disease...