Control charts are graphical tools widely used to monitor manufacturing processes to quickly detect any change in a process that may result in a change in product quality. The statistic plotted on a control chart is based on samples of n ≥ 1 observations (rational subgroups) that may be taken at regular sampling intervals. However, there are numerous practical applications using individual obse...

The Multivariate EWMA control chart, MEWMA, Lowry, Woodall, Champ and Ridgon [1] and its univariate version EWMA, may be designed to efficiently detect small shifts in the mean vector of a set of p quality characteristics of a production process. However, this work presents a method for the optimal design of MEWMA and EWMA charts parameters to control processes where it is not convenient to det...

Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts. Nevertheless, there are some disadvantages when multivariate schemes are employed. The main problem is how to interpret the out-of-control signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector...

Missing data is a common feature of large data sets in general and medical data sets in particular. Depending on the goal of statistical analysis, various techniques can be used to tackle this problem. Imputation methods consist in substituting the missing values with plausible or predicted values so that the completed data can then be analysed with any chosen data mining procedure. In this wor...

BACKGROUND A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non-random patterns in the data sequence. METHODS We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings ...

Control limits in traditional Multivariate Quality Control Charts (MQCC), such as Hotelling's T control chart, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially-Weighted Moving Average (MEWMA) control charts are based on multivariate normal assumption. This assumption is not usually satisfied, especially in real life applications. The purpose of a control chart based on suppor...

The most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the Shewhart type C-chart. In this paper, a new method is proposed in an attempt to reduce the false alarm rate in the C-chart. To do this, the decision on beliefs (DOB) concept is first uti [1] Corresponding author e-mail: [email protected]   lized to design an iterative method, where...

A memory-type control chart is an important tool of statistical process for monitoring small to moderate shifts in the manufacturing process. Using prior information by Bayesian approach helpful charts. In this paper, a new hybrid exponentially weighted moving average (HEWMA) suggested under theory using ranked set sampling (RSS) schemes posterior and predictive distribution with informative di...