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Profile monitoring for a binary response

Author

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  • Arthur Yeh
  • Longcheen Huwang
  • Yu-Mei Li

Abstract

Pertaining to industrial applications in which the response variable of interest is binary, this paper studies how the profile functional relationship between the response and predictor variables can be monitored using logistic regression. Under such a premise, several Hotelling T2 charts that have been studied under continuous response variable to binary response variable for the purpose of Phase I profile monitoring are extended. The performance of these T2 charts in terms of the signal probability for different out-of-control scenarios is compared based on simulation studies. A real example originated from aircraft construction is given in which these T2 charts are applied and compared using the data. A discussion of potential future research is also given.

Suggested Citation

  • Arthur Yeh & Longcheen Huwang & Yu-Mei Li, 2009. "Profile monitoring for a binary response," IISE Transactions, Taylor & Francis Journals, vol. 41(11), pages 931-941.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:11:p:931-941
    DOI: 10.1080/07408170902735400
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    Cited by:

    1. Luiz M A Lima-Filho & Tarciana Liberal Pereira & Tatiene C Souza & Fábio M Bayer, 2020. "Process monitoring using inflated beta regression control chart," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
    2. Unarine Netshiozwi & Ali Yeganeh & Sandile Charles Shongwe & Ahmad Hakimi, 2023. "Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    3. Keerthi Bandara & Abdel‐Salam G. Abdel‐Salam & Jeffrey B. Birch, 2020. "Model robust profile monitoring for the generalized linear mixed model for Phase I analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(6), pages 1037-1059, November.
    4. Dong Ding & Fugee Tsung & Jian Li, 2017. "Ordinal profile monitoring with random explanatory variables," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 736-749, February.

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