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 …

A unique approach to understanding the foundations of statistical quality control with a focus
on the latest developments in nonparametric control charting methodologies Statistical …

In monitoring process parameters, we assume normality of the quality characteristic of
interest, which is an ideal assumption. In many practical situations, we may not know the …

Traditional statistical process control for variables data often involves the use of a separate
mean and a standard deviation chart. Several proposals have been published recently …

Nonparametric control charts are more practical tools for statistical process control (SPC), as
they are robust in situations in which the underlying distribution is unknown …

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 …

A single distribution‐free (nonparametric) Shewhart‐type chart on the basis of the Lepage
statistic is well known in literature for simultaneously monitoring both the location and the …

Distribution-free (nonparametric) control charts provide a robust alternative to a data analyst
when there is lack of knowledge about the underlying distribution. A two-sided …

Control charts that are typically based on the assumption of a specific form of a parametric
distribution, such as the normal, are called parametric control charts. In many applications …

Nonparametric control charts are useful when there is limited or complete lack of knowledge
about the form of the underlying distribution. Though traditional statistical process control …