The main purpose of this research is to construct a chart for controlling the mean and variance together data distributed multivariate t distribution using State Space Model (SSM) through applying Bayes' Factors (BF). constructed control will undertake case when parameters' vector unknown matrix known. Then, drawing series these factors on univariate modified EWMA after assuming that has an ARM...

Micro-manufacturing processes are characterized by high process variability and an increased significance of measurement uncertainty in relation to tight tolerance specifications. Therefore, an approach that separates the superposition of measurement and manufacturing variation is demanded. A novel design for a quality control chart that makes it possible to monitor, control and extract measure...

Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of or limited knowledge about the underlying process distribution, especially when the process measurement is multivariate. This paper develops a new multivariate self-starting methodology for monitoring location parameters. It is based on adapting the multivariate spatial rank to on-line sequentia...

There have been some advances in multivariate control charts recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of normal process single chart, which is developed by improving modifying another recently proposed scheme. We not only propose but also make it adaptive varying all chart parameters. Our scheme, first time, considers two forms: “c...

Previously, quality control and improvement researchers discussed multivariate control charts for independent processes and univariate control charts for autocorrelated processes separately. We combine the two topics and propose vector autoregressive (VAR) control charts for multivariate autocorrelated processes. In addition, we estimateAR(p) models instead ofARMAmodels for the systematic cause...