In this paper, a CUSUM control chart based on data depth is considered for detecting a shift in either in the mean vector, the covariance matrix, or both of the process. The proposed chart is preferable from a robustness point of view, has attractive detection performances and can be useful in preliminary analysis setting where there is limited knowledge about the underlying process. A diagnost...

The statistical cumulative sum (CUSUM) chart is a powerful tool for monitoring the attribute quality variable in manufacturing industry. In this article, we studied the multiplicity problem caused by simultaneously monitoring more than one attribute quality variable. Multiple binomial and Poisson CUSUM charts incorporating a multiple hypothesis testing technique known as false discovery rate co...

This paper develops a model for the analysis and design of a CUSUM chart for monitoring the process mean in short production runs and proposes statistical measures of performance that are appropriate when the process is not operating indefinitely. The model can be used to evaluate the performance of the CUSUM scheme for any given set of chart parameters and thus allows comparisons with Shewhart...

We develop an empirical approximation to the null steady-state distribution of the cumulative sum (CUSUM) statistic, defined as the distribution of values obtained by running a CUSUM with no upper boundary under the null state for an indefinite period of time. The derivation is part theoretical and part empirical and the approximation is valid for CUSUMs applied to Normal data with known varian...

in this paper we present a method for optimal design of combined ewma-ewma control scheme. shewhart control chart, introduced by walter shewhart, only takes into account present information of the process, so it is insensitive in detecting small shifts. ewma and cusum control charts are good choices for detecting small shifts. a combined control scheme monitors the process for large and small s...

Control charts have been broadly used for monitoring the process mean and dispersion. Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are memory control charts as they utilize the past information in setting up the control structure. This makes CUSUM and EWMA-type charts good at detecting small disturbances in the process. This article proposes two new mem...

Usually, in monitoring schemes the nominal value of the process parameter is assumed known. However, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. On the other hand, applying a fixed control limit for the risk-adjusted Bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...

usually, in monitoring schemes the nominal value of the process parameter is assumed known. however, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. on the other hand, applying a fixed control limit for the risk-adjusted bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...

As time passes, change occurs. With this change comes the need for surveillance. One may be a technician on an assembly line and in need of a surveillance technique to monitor the number of defective components produced. On the other hand, one may be an administrator of a hospital in need of surveillance measures to monitor the number of patient falls in the hospital or to monitor surgical outc...

This article presents a single CUSUM scheme (called the GCUSUM chart) for simultaneously monitoring the time interval T and magnitude X of an event. For example, a traffic accident may be considered as an event, and the total loss in dollars in each case is the event magnitude. Since the GCUSUM chart is developed based on a synthetic statistic G which is a function of both T and X, this new cha...