IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i7p2653-2664.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00005-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Qin & Luo, Yunzhao & Wang, Zhaojun, 2010. "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1634-1645, June.
    2. Su, Yan & Shu, Lianjie & Tsui, Kwok-Leung, 2011. "Adaptive EWMA procedures for monitoring processes subject to linear drifts," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2819-2829, October.
    3. Lei Yong Lee & Michael Boon Chong Khoo & Sin Yin Teh & Ming Ha Lee, 2015. "A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    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. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    6. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Wu, Zhang & Yang, Mei & Khoo, Michael B.C. & Castagliola, Philippe, 2011. "What are the best sample sizes for the Xbar and CUSUM charts?," International Journal of Production Economics, Elsevier, vol. 131(2), pages 650-662, June.
    8. Ou, Yanjing & Wu, Zhang & Tsung, Fugee, 2012. "A comparison study of effectiveness and robustness of control charts for monitoring process mean," International Journal of Production Economics, Elsevier, vol. 135(1), pages 479-490.
    9. Ugaz Sánchez, Willy Ericson & Sánchez, Ismael, 2015. "Adaptive EWMA Control Charts with a Time Varying Smoothing Parameter," DES - Working Papers. Statistics and Econometrics. WS ws1507, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Zhang, Jiujun & Li, Zhonghua & Wang, Zhaojun, 2010. "A multivariate control chart for simultaneously monitoring process mean and variability," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2244-2252, October.
    11. Zhang, Min & Nie, Guohua & He, Zhen, 2014. "Performance of cumulative count of conforming chart of variable sampling intervals with estimated control limits," International Journal of Production Economics, Elsevier, vol. 150(C), pages 114-124.
    12. Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.
    13. Luo, Yunzhao & Li, Zhonghua & Wang, Zhaojun, 2009. "Adaptive CUSUM control chart with variable sampling intervals," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2693-2701, May.
    14. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    15. Yafen Liu & Zhen He & M. Shamsuzzaman & Zhang Wu, 2010. "A combined control scheme for monitoring the frequency and size of an attribute event," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 1991-2013.
    16. Lee, Pei-Hsi, 2011. "Adaptive R charts with variable parameters," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 2003-2010, May.
    17. 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.
    18. Pei-Hsi Lee & Yi-Hsien Huang & Tsen-I Kuo & Ching-Cheng Wang, 2013. "The effect of the individual chart with variable control limits on the river pollution monitoring," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1803-1812, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2653-2664. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.