IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v41y2014i1p200-214.html

Control chart for monitoring multivariate COM-Poisson attributes

Author

Listed:
  • Aamir Saghir
  • Zhengyan Lin

Abstract

Statistical process control of multi-attribute count data has received much attention with modern data-acquisition equipment and online computers. The multivariate Poisson distribution is often used to monitor multivariate attributes count data. However, little work has been done so far on under- or over-dispersed multivariate count data, which is common in many industrial processes, with positive or negative correlation. In this study, a Shewhart-type multivariate control chart is constructed to monitor such kind of data, namely the multivariate COM-Poisson (MCP) chart, based on the MCP distribution. The performance of the MCP chart is evaluated by the average run length in simulation. The proposed chart generalizes some existing multivariate attribute charts as its special cases. A real-life bivariate process and a simulated trivariate Poisson process are used to illustrate the application of the MCP chart.

Suggested Citation

  • Aamir Saghir & Zhengyan Lin, 2014. "Control chart for monitoring multivariate COM-Poisson attributes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 200-214, January.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:200-214
    DOI: 10.1080/02664763.2013.838666
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2013.838666
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2013.838666?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jing-Er Chiu & Tsen-I Kuo, 2010. "Control charts for fraction nonconforming in a bivariate binomial process," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1717-1728.
    2. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
    3. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
    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. Kokonendji, Célestin C. & Puig, Pedro, 2018. "Fisher dispersion index for multivariate count distributions: A review and a new proposal," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 180-193.
    2. Muhammad Aslam & Ali Hussein Al-Marshadi, 2019. "Design of a Control Chart Based on COM-Poisson Distribution for the Uncertainty Environment," Complexity, Hindawi, vol. 2019, pages 1-9, July.

    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. Andrea Pellegrini & Riccardo Scarpa & Maria De Salvo & Laura Giuffrida & Giovanni Signorello, 2026. "Geographical and environmental dependencies in multivariate count models: Recreation demand for Italian national parks," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 28(2), pages 679-702, April.
    2. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    3. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    4. Molly C. Klanderman & Kathryn B. Newhart & Tzahi Y. Cath & Amanda S. Hering, 2020. "Fault isolation for a complex decentralized waste water treatment facility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 931-951, August.
    5. Rose Baker, 2026. "A model for underdispersed count data," Statistical Papers, Springer, vol. 67(2), pages 1-14, April.
    6. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    7. Fernando Bonassi & Rafael Stern & Cláudia Peixoto & Sergio Wechsler, 2015. "Exchangeability and the law of maturity," Theory and Decision, Springer, vol. 78(4), pages 603-615, April.
    8. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    9. Dexter Cahoy & Elvira Di Nardo & Federico Polito, 2021. "Flexible models for overdispersed and underdispersed count data," Statistical Papers, Springer, vol. 62(6), pages 2969-2990, December.
    10. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    11. Sellers, Kimberly F. & Raim, Andrew, 2016. "A flexible zero-inflated model to address data dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 68-80.
    12. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    13. Mark E. Piatek & Dankmar Böhning, 2024. "Deriving a zero-truncated modelling methodology to analyse capture–recapture data from self-reported social networks," METRON, Springer;Sapienza Università di Roma, vol. 82(2), pages 135-160, August.
    14. Zhou, Can & Jiao, Yan & Browder, Joan, 2019. "K-aggregated transformation of discrete distributions improves modeling count data with excess ones," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    15. Teerawat Simmachan & Noppachai Wongsai & Sangdao Wongsai & Rattana Lerdsuwansri, 2022. "Modeling road accident fatalities with underdispersion and zero-inflated counts," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-23, November.
    16. Wen-An Yang, 2016. "Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 845-874, August.
    17. Rodrigues, Josemar & Balakrishnan, N. & Cordeiro, Gauss M. & de Castro, Mário, 2011. "A unified view on lifetime distributions arising from selection mechanisms," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3311-3319, December.
    18. Srivatsa Vasudevan & Seng Huat Ong & Choung Min Ng, 2026. "On flexible affine weighted Poisson models for count data," Statistical Papers, Springer, vol. 67(2), pages 1-23, April.
    19. Florez Mauro & Guindani Michele & Vannucci Marina, 2025. "Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 21(1), pages 51-71.
    20. Rajib Dey & M. Ataharul Islam, 2017. "A conditional count model for repeated count data and its application to GEE approach," Statistical Papers, Springer, vol. 58(2), pages 485-504, 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:taf:japsta:v:41:y:2014:i:1:p:200-214. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.