Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality of the products or services. In the literature, a mixed exponentially weighted moving average–moving average (EWMA–MA) control chart for monitoring the process location is proposed to enhance the overall shift detection ability of the EWMA control chart. It is noted that the moving averages terms were considered as independent irrespective of their order. Consequently, the covariance terms are ignored while deriving the variance expression of the monitoring statistic. However, the successive moving averages of span w might not be independent since each term includes w − 1 preceding samples’ information. In this study, the variance expression of the mixed EWMA-MA charting statistic is derived by considering the dependency among the sequential moving averages. The control limits of the mixed EWMA-MA control chart are revised and the run-length profile is studied by using Monte Carlo simulations. The performance of the mixed EWMA-MA chart is compared with the existing counterparts and its robustness under various process distributions is studied. In the end, a real-life example is provided to demonstrate its application by using the data from a combined cycle power plant.

This article considers the problem of jointly monitoring the mean and variance of a process by multi-chart schemes. Multi-chart is a combination of several single charts which detects changes in a process quickly. Asymptotic analyses and simulation studies show that the optimized CUSUM multi-chart has optimal performance than optimized EWMA multi-chart in jointly detecting mean and variance shifts in an $i.i.d.$ normal observation. A real example that monitors the changes in IBM's stock returns (mean) and risks (variance) is used to demonstrate the performance of the above two multi-charts. The proposed method has been compared to a benchmark and it performed better.

Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.

This article not only shows that the CUSUM multi-chart which consists of several CUSUM charts, has the asymptotic optimal performance in jointly detecting and diagnosing the unknown change in a sequence of $i.i.d.$ observations but also provides a design method of optimizing the CUSUM and EWMA multi-charts. The numerical comparisons illustrate that the optimized CUSUM multi-chart has better performance in jointly detecting and diagnosing the mean and variance shifts in $i.i.d.$ normal observations than that of the optimized EWMA multi-chart. A real example for engineering surveillance using the electric power generation data was used to demonstrate the practicality of the schemes.

In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana's macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana's principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable.

In recent times, researchers have resorted to using trigonometric and error function‐generated families of distributions for extending baseline distributions due to their parsimonious and flexible nature. The study proposed the sine error function‐generated family of distributions and derived the statistical properties of the proposed family. Four special models based on the proposed family are presented. The hazard rate functions of the special models exhibit various forms such as increasing, decreasing and upside‐down bathtub shapes. The study estimated the parameters of the formulated family using four estimation techniques, and a simulation study was conducted to assess the behaviour of the estimators. The study illustrated the applications of the proposed family using two real‐life datasets, and it was revealed that the sine error function Weibull distribution provided the best fit to the datasets in all cases.