American Journal of Theoretical and Applied Statistics

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Comparative Analysis of Bayesian Control Chart Estimation and Conventional Multivariate Control Chart

Received: 15 January 2013    Accepted:     Published: 10 January 2013
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Abstract

Bayesian model or Beta-binomial conjugate using Bayesian sequential estimation method to estimate the proportion of different age groups is compared with the conventional multivariate control chart method. The parameters for the techniques were derived and applied. The result shows that the patients between the ages of 15-44 in 2009 and 44-64 and 64 and above in 2011 are out of control. This implies the Bayesian sequential estimation method is very efficient to notice any small shift that occurs among patients that make use of the hospital. Also the bracket mentioned above was very high among the people that used the hospital compared to others. The result of 2011shows that there was a high shift in the ages of the patients that attended the hospital for the ages between 44-64 and 64 and above respectively.

DOI 10.11648/j.ajtas.20130201.12
Published in American Journal of Theoretical and Applied Statistics (Volume 2, Issue 1, January 2013)
Page(s) 7-11
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Beta-Binomial, Sequential Estimation, Hyperparameters, Conjugates Beta-Binomial, Shrinkage Factor And Multivariate Random Variables

References
[1] Resul Oduk (2012) Control Charts for Serially Dependent Multivariate Data Thesis submitted to the Department of In-formatics and Mathematical Modeling at Technical University of Denmark in partial fulfillment of the requirements for the degree of Master of Science in Mathematical Modeling and Computation Technical University Of Denmark.
[2] Shewart W.A (1952) The Application of Statistics as an aid in maintaining quality of a manufactured product" Journal of American Statistical Association: 546-548.
[3] Shewhart WA, (1986), Statistical Method from the Viewpoint of Quality Control General Publishing Company, ISBN 0-486-65232-7.
[4] Shewhart WA, Economic control of quality of manufactured product (1931), Princeton, NJ:Reinhold Co.
[5] Woodall, W. H. (2000). "Controversies and Contradictions in Statistical Process Control" (with discussion). Journal of Quality Technology 32, pp. 341–378. (available at ww.asq.Org/pub/jqt).
[6] Woodall, W.H. Review of Improving Healthcare with Control Charts by Raymond G. Carey, Journal of Quality Technology, 36, 336-338 (2004). 23.
[7] Woodall, W.H. Use of control charts in health-care and pub-lic-health surveillance (with discussion), Journal of Quality Technology, 38, 89-104 (2006).
[8] Lee, P. (2004), Bayesian Statistics An Introduction, Hodder Arnold, New York.
[9] Brandel, J. (2004), Empirical Bayes Methods for missing data analysis, Department of Mathematics Uppsala University, Project Report.
[10] Carlin, B. P. and Louis, T. A. (2000b), Bayes and Empirical Bayes Methods for Data Analysis, Boca Raton, Florida: Chapman and Hall/CRC Press.
[11] Richard A.J. and Dean W.W.(1988) Second Edition Applied Multivariate Statistical Analysis Prentice, Hall International, Inc. 607.
Cite This Article
  • APA Style

    Johnson Ademola Adewara1, J. Ademola, Ogundeji K. Rotimi. (2013). Comparative Analysis of Bayesian Control Chart Estimation and Conventional Multivariate Control Chart. American Journal of Theoretical and Applied Statistics, 2(1), 7-11. https://doi.org/10.11648/j.ajtas.20130201.12

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    ACS Style

    Johnson Ademola Adewara1; J. Ademola; Ogundeji K. Rotimi. Comparative Analysis of Bayesian Control Chart Estimation and Conventional Multivariate Control Chart. Am. J. Theor. Appl. Stat. 2013, 2(1), 7-11. doi: 10.11648/j.ajtas.20130201.12

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    AMA Style

    Johnson Ademola Adewara1, J. Ademola, Ogundeji K. Rotimi. Comparative Analysis of Bayesian Control Chart Estimation and Conventional Multivariate Control Chart. Am J Theor Appl Stat. 2013;2(1):7-11. doi: 10.11648/j.ajtas.20130201.12

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  • @article{10.11648/j.ajtas.20130201.12,
      author = {Johnson Ademola Adewara1 and J. Ademola and Ogundeji K. Rotimi},
      title = {Comparative Analysis of Bayesian Control Chart Estimation and Conventional Multivariate Control Chart},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {2},
      number = {1},
      pages = {7-11},
      doi = {10.11648/j.ajtas.20130201.12},
      url = {https://doi.org/10.11648/j.ajtas.20130201.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20130201.12},
      abstract = {Bayesian model or Beta-binomial conjugate using Bayesian sequential estimation method to estimate the proportion of different age groups is compared with the conventional multivariate control chart method. The parameters for the techniques were derived and applied. The result shows that the patients between the ages of 15-44 in 2009 and 44-64 and 64 and above in 2011 are out of control. This implies the Bayesian sequential estimation method is very efficient to notice any small shift that occurs among patients that make use of the hospital. Also the bracket mentioned above was very high among the people that used the hospital compared to others. The result of 2011shows that there was a high shift in the ages of the patients that attended the hospital for the ages between 44-64 and 64 and above respectively.},
     year = {2013}
    }
    

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    AB  - Bayesian model or Beta-binomial conjugate using Bayesian sequential estimation method to estimate the proportion of different age groups is compared with the conventional multivariate control chart method. The parameters for the techniques were derived and applied. The result shows that the patients between the ages of 15-44 in 2009 and 44-64 and 64 and above in 2011 are out of control. This implies the Bayesian sequential estimation method is very efficient to notice any small shift that occurs among patients that make use of the hospital. Also the bracket mentioned above was very high among the people that used the hospital compared to others. The result of 2011shows that there was a high shift in the ages of the patients that attended the hospital for the ages between 44-64 and 64 and above respectively.
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Author Information
  • Distance Learning Institute, University of Lagos, Lagos, Nigeria

  • Distance Learning Institute, University of Lagos, Lagos, Nigeria

  • Department of Mathematics, Faculty of Science, University of Lagos, Lagos, Nigeria

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