Trend Autoregressive Model Exact Run Length Evaluation on a Two-Sided Extended EWMA Chart

The Extended Exponentially Weighted Moving Average(extended EWMA)control chart is one of the control charts and can be used to quickly detect a small shift.The performance of control charts can be evaluated with the average run length(ARL).Due to the deriving explicit formulas for the ARL on a two-sided extended EWMA control chart for trend autoregressive or trend AR(p)model has not been reported previously.The aim of this study is to derive the explicit formulas for the ARL on a two-sided extended EWMA con-trol chart for the trend AR(p)model as well as the trend AR(1)and trend AR(2)models with exponential white noise.The analytical solution accuracy was obtained with the extended EWMA control chart and was compared to the numer-ical integral equation(NIE)method.The results show that the ARL obtained by the explicit formula and the NIE method is hardly different,but the explicit for-mula can help decrease the computational(CPU)time.Furthermore,this is also expanded to comparative performance with the Exponentially Weighted Moving Average(EWMA)control chart.The performance of the extended EWMA control chart is better than the EWMA control chart for all situations,both the trend AR(1)and trend AR(2)models.Finally,the analytical solution of ARL is applied to real-world data in the healthfield,such as COVID-19 data in the United Kingdom and Sweden,to demonstrate the efficacy of the proposed method.

A New Modified EWMA Control Chart for Monitoring Processes Involving Autocorrelated Data

Control charts are one of the tools in statistical process control widely used for monitoring,measuring,controlling,improving the quality,and detecting problems in processes in variousfields.The average run length(ARL)can be used to determine the efficacy of a control chart.In this study,we develop a new modified exponentially weighted moving average(EWMA)control chart and derive explicit formulas for both one and the two-sided ARLs for a p-order autoregressive(AR(p))process with exponential white noise on the new modified EWMA control chart.The accuracy of the explicit formulas was compared to that of the well-known numerical integral equation(NIE)method.Although both methods were highly consistent with an absolute percentage difference of less than 0.00001%,the ARL using the explicit formulas method could be computed much more quickly.Moreover,the performance of the explicit formulas for the ARL on the new modified EWMA control chart was better than on the modified and standard EWMA control charts based on the relative mean index(RMI).In addition,to illustrate the applicability of using the proposed explicit formulas for the ARL on the new modified EWMA control chart in practice,the explicit formulas for the ARL were also applied to a process with real data from the energy and agriculturalfields.

Exact Run Length Evaluation on a Two-Sided Modified ExponentiallyWeighted Moving Average Chart for Monitoring

A modified exponentially weighted moving average (EWMA) scheme is one of the quality control charts suchthat this control chart can quickly detect a small shift. The average run length (ARL) is frequently used for theperformance evaluation on control charts. This paper proposes the explicit formula for evaluating the average runlength on a two-sided modified exponentially weighted moving average chart under the observations of a first-orderautoregressive process, referred to as AR(1) process, with an exponential white noise. The performance comparisonof the explicit formula and the numerical integral technique is carried out using the absolute relative change forchecking the correct formula and the CPU time for testing speed of calculation. The results show that the ARL ofthe explicit formula and the numerical integral equation method are hardly different, but this explicit formula ismuch faster for calculating the ARL and offered accurate values. Furthermore, the cumulative sum, the classicalEWMA and the modified EWMA control charts are compared and the results show that the latter is better for smalland intermediate shift sizes. In addition, the explicit formula is successfully applied to real-world data in the healthfield as COVID-19 data in Thailand and Singapore.