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An EWMA chart for monitoring the process standard deviation when parameters are estimated

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  • Maravelakis, Petros E.
  • Castagliola, Philippe

Abstract

The EWMA chart for the standard deviation is a useful tool for monitoring the variability of a process quality characteristic. The performance of this chart is usually evaluated under the assumption of known parameters. However, in practice, process parameters are estimated from an in-control Phase I data set. A modified EWMA control chart is proposed for monitoring the standard deviation when the parameters are estimated. The Run Length properties of this chart are studied and its performance is evaluated by comparing it with the same chart but with process parameters assumed known.

Suggested Citation

  • Maravelakis, Petros E. & Castagliola, Philippe, 2009. "An EWMA chart for monitoring the process standard deviation when parameters are estimated," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2653-2664, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2653-2664
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    References listed on IDEAS

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    1. Galeano, Pedro, 2007. "The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson Process," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6151-6165, August.
    2. Shu, Lianjie & Jiang, Wei & Wu, Zhang, 2008. "Adaptive CUSUM procedures with Markovian mean estimation," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4395-4409, May.
    3. Wu, Zhang & Yang, Mei & Jiang, Wei & Khoo, Michael B.C., 2008. "Optimization designs of the combined Shewhart-CUSUM control charts," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 496-506, December.
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    Cited by:

    1. Huang, Wenpo & Shu, Lianjie & Jiang, Wei, 2012. "Evaluation of exponentially weighted moving variance control chart subject to linear drifts," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4278-4289.
    2. Human, S.W. & Chakraborti, S. & Smit, C.F., 2010. "Shewhart-type control charts for variation in phase I data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 863-874, April.
    3. Axel Gandy & Jan Terje Kvaløy, 2013. "Guaranteed Conditional Performance of Control Charts via Bootstrap Methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 647-668, December.
    4. Lee, Pei-Hsi, 2013. "Joint statistical design of X¯ and s charts with combined double sampling and variable sampling interval," European Journal of Operational Research, Elsevier, vol. 225(2), pages 285-297.
    5. Yingjie Duan & Hong Ni & Xiaoyong Zhu, 2022. "A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking," Future Internet, MDPI, vol. 14(4), pages 1-15, March.
    6. Jose Luis Alfaro & Juan Fco. Ortega, 2019. "A new multivariate variability control chart based on a covariance matrix combination," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 823-836, May.
    7. Bersimis, Sotiris & Koutras, Markos V. & Maravelakis, Petros E., 2014. "A compound control chart for monitoring and controlling high quality processes," European Journal of Operational Research, Elsevier, vol. 233(3), pages 595-603.
    8. Khoo, Michael B.C. & Teoh, W.L. & Castagliola, Philippe & Lee, M.H., 2013. "Optimal designs of the double sampling X¯ chart with estimated parameters," International Journal of Production Economics, Elsevier, vol. 144(1), pages 345-357.
    9. Johannssen, Arne & Chukhrova, Nataliya & Castagliola, Philippe, 2022. "The performance of the hypergeometric np chart with estimated parameter," European Journal of Operational Research, Elsevier, vol. 296(3), pages 873-899.
    10. Huwang, Longcheen & Huang, Chun-Jung & Wang, Yi-Hua Tina, 2010. "New EWMA control charts for monitoring process dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2328-2342, October.
    11. Chi-Shuan Liu & Fang-Chih Tien, 2011. "A single-featured EWMA- X control chart for detecting shifts in process mean and standard deviation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2575-2596, January.
    12. H. You & Michael Khoo & P. Castagliola & Yanjing Ou, 2015. "Side sensitive group runs $$\bar{{X}}$$ X ¯ chart with estimated process parameters," Computational Statistics, Springer, vol. 30(4), pages 1245-1278, December.
    13. Ugaz Sánchez, Willy Ericson & Alonso Fernández, Andrés Modesto & Sánchez Rodríguez-Morcillo, Ismael, 2016. "Monitoring variance by EWMA charts with time varying smoothing parameter," DES - Working Papers. Statistics and Econometrics. WS 23413, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Graham, M.A. & Mukherjee, A. & Chakraborti, S., 2012. "Distribution-free exponentially weighted moving average control charts for monitoring unknown location," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2539-2561.

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