CUSUM control chart for Katz family of distributions

카즈분포족에 대한 누적합 관리도

  • Cho, Gyo-Young (Department of Statistics, Kyungpook National University)
  • Received : 2010.10.22
  • Accepted : 2010.12.17
  • Published : 2011.01.31

Abstract

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting scheme in practice. And CUSUM-chart is used when it is desirable to detect out of control situations very quickly because of sensitive to a small or gradual drift in the process. In this paper, I compare CUSUM-chart to X-chart for the Katz family covering equi-, under-, and over-dispersed distributions relative to the Poisson distribution.

결점수를 모니터링하기 위한 통계적 공정관리는 생산공정에 널리 사용된다. 결점 수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 누적합 관리도는 공정의 작은 변화를 찾는데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 Katz 분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 누적합 관리도의 효율을 평균런의길이에 근거하여 비교 한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 누적합 관리도를 비교하는 것이다.

Keywords

References

  1. Fang, Y. (2002). GMM tests for the Katz family of distributions. Journal of Statistical Planning and Inference, 110 , 77-95.
  2. Fang, Y. (2003). c-chart, X-chart, and the Katz family of distributions. Journal of QualityTechnology, 35, 104 - 114.
  3. Gurland, J. (1983). Katz system of distributions in Encyclopedia of Statistical Sciences, Edited by S. I. Kotz and N. L. Johnson, John Wiley & Sons, New York.
  4. Heimann, P. A. (1996). Attributes control charts with large sample sizes. Journal of Quality Technology, 28, 451-459. https://doi.org/10.1080/00224065.1996.11979703
  5. Im, C. D. and Cho, G. Y. (2009). Multiparameter CUSUM charts with variable sampling intervals. Journal of the Korean Data & Information Science Society, 20, 593-599.
  6. Johnson, N. L. and Kotz, S. I. (1969). Discrete distributions, John Wiley & Sons Inc., New York.
  7. Katz, L. (1963). United treatment of a board class of discrete probability distributions. Proceedings of the international symposium on discrete distributions, Montreal, Canada.
  8. Na, O. K., Ko, B. W. and Lee, S. Y. (2010). CUSUM of squares test for discretely observed sample from multidimensional diffusion processes. Journal of the Korean Data & Information Science Society, 21, 547-554.
  9. Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100-114. https://doi.org/10.1093/biomet/41.1-2.100
  10. Reynolds, M. R., Jr., Amin, R. W. and Arnold, J. C. (1990). CUSUM charts with variable sampling intervals. Technometrics, 32, 371-384. https://doi.org/10.1080/00401706.1990.10484721
  11. Ryan. T. P. and Schwertman, N. C. (1997). Optimal limits for attributes control charts. Journal of Quality Technology, 29, 86-98. https://doi.org/10.1080/00224065.1997.11979728
  12. Wadsworth, H. M., Stephens, K. S. and Godfrey, A. B. (1986). Modern methods for quality control and improvement, John Wiley & Sons, New York.
  13. Wheeler, D. J. (1995). Advanced topics in statistical process control, SPC Press, Inc., Knoxville, TN.