PARAMETRIC AND NONPARAMETRIC METHODS OF STATISTICAL PROCESS CONTROL

A Review and Evaluation of Statistical Process Control Methods in Monitoring Process Mean and Variance Simultaneously

In this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single process mean and variance, are reviewed. Then, by designing proper simulation studies these methods are evaluated in terms of in-control and out-ofcontrol average run length criteria (ARL). The results of these simulation experiments show that the EWMA and EWMS methods ...

Handbook of Parametric and Nonparametric Statistical Procedures

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Evaluation of Seed Yield Stability of Promising Sesame Lines using Different Parametric and Nonparametric Methods

Analysis of genotype by environment interaction using different statistical methods is very important in plant breeding. In order to evaluate the seed yield stability of promising sesame lines using different parametric and non-parametric statistics, an experiment was conducted using 13 promising sesame lines with check variety Oltan at three locations of Karaj, Mashhad, and Moghan (Iran) in a ...

On Nonparametric Statistical Process Control of Univariate Processes

This paper considers statistical process control (SPC) of univariate processes when the parametric form of the process distribution is unavailable. Most existing SPC procedures are based on the assumption that a parametric form (e.g., normal) of the process distribution can be specified beforehand. In the literature, it has been demonstrated that their performance is unreliable in cases when th...