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On limiting theorems for conditional causation probabilities of multiple-run-rules

Author

Listed:
  • Chang, Hsing-Ming
  • Chang, Yung-Ming
  • Fu, Winnie H.W.
  • Lee, Wan-Chen

Abstract

Causation probabilities of multiple-run-rules based on patterns of multi-state trials have been used and studied in various fields such as quality control, reliability of engineering system, biology, DNA sequence analysis and survival analysis. In this manuscript, we derive the limiting results for two types of conditional causation probabilities for multiple-run-rules by using the finite Markov chain imbedding technique. Extension and numerical examples are given to illustrate the theoretical results.

Suggested Citation

  • Chang, Hsing-Ming & Chang, Yung-Ming & Fu, Winnie H.W. & Lee, Wan-Chen, 2018. "On limiting theorems for conditional causation probabilities of multiple-run-rules," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 151-156.
    • Handle: RePEc:eee:stapro:v:138:y:2018:i:c:p:151-156
    DOI: 10.1016/j.spl.2018.02.069
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    References listed on IDEAS

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    1. M. V. Koutras & S. Bersimis & P. E. Maravelakis, 2007. "Statistical Process Control using Shewhart Control Charts with Supplementary Runs Rules," Methodology and Computing in Applied Probability, Springer, vol. 9(2), pages 207-224, June.
    2. 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.
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    Cited by:

    1. Hsing-Ming Chang & James C. Fu, 2022. "On Distribution and Average Run Length of a Two-Stage Control Process," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2723-2742, December.

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