Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

Control charts that are based on assumption (s) of a specific form for the underlying process
distribution are referred to as parametric control charts. There are many applications where …

We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …

The change point model framework introduced in Hawkins, Qiu, and Kang (2003) and
Hawkins and Zamba (2005a) provides an effective and computationally efficient method for …

Statistical process control (SPC) charts play a central role in quality control and
management. Many conventional SPC charts are designed under the assumption that the …

Most traditional control charts used for sequential monitoring assume that full knowledge is
available regarding the prechange distribution of the process. This assumption is unrealistic …

The assumption of fully known in-control distributions has long been recognized as an
idealization, at best approximately true. Recent development of normal-based change-point …

In the control chart literature, a number of one-and two-chart schemes has been developed
to simultaneously monitor the mean and variance parameters of normally distributed …

Control charts are powerful tools to observe the presentation of the manufacturing process.
Mostly, when the data in industries come from the process may follow non-normal or …

While the assumption of normality is required for the validity of most of the available control
charts for joint monitoring of unknown location and scale parameters, we propose and study …