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 …

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 …

An overview of monitoring schemes from a class called generally weighted moving average
(GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted …

Abstract “Big data” is a buzzword these days due to an enormous amount of data-rich
applications in different industries and research projects. In practice, big data often take the …

Abstract The AEWMA (Adaptive Exponentially Weighted Moving Average) chart is a scheme
that combines the Shewhart and the classical EWMA charts in a smooth way. In this paper, a …

We generalize popular distribution-free (nonparametric) Shewhart–Lepage scheme for
simultaneously monitoring of location and scale parameters using an adaptive approach …

A new single distribution-free Phase-II CUSUM procedure based on the Cucconi statistic for
simultaneously monitoring shifts in the unknown location and scale parameters of a process …

This paper proposes a new distribution-free control chart by integrating the powerful
nonparametric two-sample Cramér-von Mises test and the exponentially weighted moving …

Traditional statistical process control charts are based on the assumptions that process
observations are independent and identically normally distributed when the related process …

Multivariate statistical process control (MSPC) charts are particularly useful when there is a
need to simultaneously monitor several quality characteristics of a process. Most of the …