The Accurate Results of Average Run Length on Modified EWMA Control Chart for the First-Order Moving Average Process with Exogenous Variables Models
Keywords:
ARL, MAX process, explicit formulas, monitoring process, explanatory variableAbstract
In this study, we derive explicit formulas for the average run length (ARL) of a first-order moving average process with exogenous variables (MAX(1,1)) with exponential white noise and compare their performance on standard and modified and exponentially weighted moving average (EWMA) control charts based on the absolute percentage relative error (APRE) and the relative mean index (RMI). Moreover, we compare the accuracy and CPU time of the explicit formulas with the ARL based on the numerical integral equation (NIE) method derived by using the Gauss-Legendre quadrature rule for the same process on the two control charts. To demonstrate the capability of our explicit formulas approach, we applied it to two real datasets: the closing stock prices for the PTT public company limited with the THB/USD daily foreign exchange rate as the exogenous variable and the monthly gold futures price with the crude oil futures price as the exogenous variable. The results of applying the ARL based on the explicit formulas with the two real datasets show that the modified EWMA control chart performed better than the EWMA control chart under these circumstances.
References
Alpaben KP, Jyoti D. Modified exponentially weighted moving average (EWMA) control chart for an analytical process data. J Chem Eng Mater Sci. 2011; 2(1): 12-20.
Andel J. On AR(1) processes with exponential white noise. Commun Stat - Theory Methods. 1988; 17(5): 1481-1495.
Areepong Y. Explicit formulas of average run length for a moving average control chart for monitoring the number of defective products. Int J Pure Appl Math. 2012; 80(3): 331-343.
Barbeito I, Zaragoza S, Tarrío-Saavedra J, Naya S. Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Appl Energy. 2017; 190: 1-7.
Fusion media limited. (2022, September 9). World Financial Markets. Retrieved from https://www. investing.com/markets.
Ibazizen M, Fellag H. Bayesian estimation of an AR(1) process with exponential white noise. Statistics. 2003; 37(5): 365-72.
Khan N, Aslam M, Jun CH. Design of a control chart using a modified EWMA statistic. Qual Reliab Eng Int. 2017; 33(5): 1095-1104.
Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics. 1990; 32(1): 1-2.
Mastrangelo CM, Montgomery DC. SPC with correlated observations for the chemical and process industries. Qual Reliab Eng Int. 1995; 11(2): 79-89.
Page ES. Continuous inspection schemes. Biometrika. 1954; 41(1/2): 100-105.
Paichit P. An integral equation procedure for average run length of control chart of ARX(p) processes. Far East J Math Sci. 2016; 99(3), https://doi.10.17654/MS099030359.
Petcharat K, Sukparungsee S, Areepong Y. Exact solution of the average run length for the cumulative sum chart for a moving average process of order q. ScienceAsia. 2015; 41: 141-147.
Roberts SW. Control chart tests based on geometric moving averages. Technometrics. 1959; 239-250.
Serel DA. Economic design of EWMA control charts based on loss function. Math Comput Model. 2009; 49(3-4): 745-759.
Shewhart WA. Economic control of quality of manufactured product. Macmillan and Co Ltd, London; 1931.
Silpakob K, Areepong Y, Sukparungsee S, Sunthornwat R. Explicit analytical solutions for the average run length of modified EWMA control chart for ARX(p, r) processes. Songklanakarin J Sci Technol. 2021; 43(5): 1414-1427.
Sukparungsee S, Areepong Y. A study of the performance of EWMA chart with transformed Weibull observations. Thail Stat. 2009; 7(2): 179-191.
Supharakonsakun Y, Areepong Y, Sukparungsee S. The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data. PeerJ. 2020; 8: e10467.
Turkman M. Bayesian analysis of an autoregressive process with exponential white noise. Statistics. 1990; 21(4): 601-608.
Vanbrackle III LN, Reynolds Jr MR. EWMA and CUSUM control charts in the presence of correlation. Commun Stat - Simul Comput. 1997; 26(3): 979-1008.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.