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Monitoring correlated processes with binomial marginals

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  • Christian Weiss

Abstract

Few approaches for monitoring autocorrelated attribute data have been proposed in the literature. If the marginal process distribution is binomial, then the binomial AR(1) model as a realistic and well-interpretable process model may be adequate. Based on known and newly derived statistical properties of this model, we shall develop approaches to monitor a binomial AR(1) process, and investigate their performance in a simulation study. A case study demonstrates the applicability of the binomial AR(1) model and of the proposed control charts to problems from statistical process control.

Suggested Citation

  • Christian Weiss, 2009. "Monitoring correlated processes with binomial marginals," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(4), pages 399-414.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:399-414
    DOI: 10.1080/02664760802468803
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    References listed on IDEAS

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    1. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(3), pages 319-341, August.
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    Cited by:

    1. Scotto, Manuel G. & Weiß, Christian H. & Silva, Maria Eduarda & Pereira, Isabel, 2014. "Bivariate binomial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 233-251.

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