Although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. Identifying the real time of the process changes is known as change-point estimation problem. There are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. In this paper we propose a novel app...

Not all data in practice came from a process with normal distribution. When the process distribution is non-normal or unknown, the commonly used Shewhart control charts are not suitable. In this paper, a new non-parametric CUSUM Mean Chart is proposed to monitor the possible small mean shifts in the process. The sampling properties of the new monitoring statistics are examined and the average r...

Traditional control charts are commonly based on the averages of the inspected groups of observations. It turns out to be quite worthwhile to consider alternative approaches. In particular, a very good proposal is to use instead the group minimum for comparison to some suitable upper limit (and likewise the group maximum for comparison to a lower limit). The power of detection during Out-of-Con...

The mixed control chart is proposed to improve detection performance with fewer process shifts. In this study, we the modified exponentially weighted moving average - (MMEM), a new for observing changes in mean. Average run length, standard deviation of and median length can be used examine effectiveness detecting Shewhart, Moving (MA), Modified Exponentially Weighted (MEWMA), Mixed (MMME) char...

The Burr type XII has been studied for various applications of lifetime modelings. However, control charts related to the Burr type XII percentiles have not been seen in the literature. In this paper, three control charts for the Burr type XII percentiles are investigated. An extensive Monte Carlo simulation study is conducted to compare among the Shewhart-type chart, and parametric bootstrap c...

Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation charts are introduced as techniques for ensurin...

Monitoring Markov Dependent Observations with a Log-Likelihood Based CUSUM Shabnam Mousavi and Marion R. Reynolds, Jr. Department of Statistics, The Pennsylvania State University, University Park, PA 16802-2111, U.S.A., Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany, Departments of Statistics and Forestry, Virginia Polytechnic Institute and State University, B...

When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

In statistical process control, the control charts are the most powerful tools for assessing the process behaviour. The Shewhart S chart is a standard tool for determining process variability. Similar to S chart, the chart based on Median Absolute Deviation from the sample median namely MAD estimator is also considered robust for both normal and non-normal processes. As ̅ and are considered unbi...