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Research Article

A method for robust design of products or processes with categorical response

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References

  • Agresti, A. 2010. Analysis of ordinal categorical data . 2nd ed. New York: John Wiley & Sons.
  • Akteke-Öztürk, B. , G. Köksal , and G. W. Weber . 2018. Nonconvex optimization of desirability functions. Quality Engineering 30 (2):293–310. doi: 10.1080/08982112.2017.1315136.
  • Asiabar, M. H. , and S. M. T. F. Ghomi . 2006. Analysis of ordered categorical data using expected loss minimization. Quality Engineering 18 (2):117–21. doi: 10.1080/08982110600567467.
  • Blair, J. , and M. G. Lacy . 1996. Measures of variation for ordinal data as functions of the cumulative distribution. Perceptual and Motor Skills 82 (2):411–8. doi: 10.2466/pms.1996.82.2.411.
  • Brant, R. 1990. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics 46 (4):1171–8. doi: 10.2307/2532457.
  • Chipman, H. , and M. Hamada . 1996. Bayesian analysis of ordered categorical data from industrial experiments. Technometrics 38 (1):1–10. doi: 10.1080/00401706.1996.10484411.
  • Costa, N. R. P. 2010. Simultaneous optimization of mean and standard deviation. Quality Engineering 22 (3):140–9. doi: 10.1080/08982110903394205.
  • D’Ambra, L. , O. Köksoy , and B. Simonetti . 2009. Cumulative correspondence analysis of ordered categorical data from industrial experiments. Journal of Applied Statistics 36 (12):1315–28. doi: 10.1080/02664760802638090.
  • Derringer, G. , and R. Suich . 1980. Simultaneous optimization of several response variables. Journal of Quality Technology 12 (4):214–9. doi: 10.1080/00224065.1980.11980968.
  • Erdural, S. , G. Köksal , and Ö. İlk . 2006. A method for analysis of categorical data for robust product or process design. In Proceedings of the 17th Conference of IASC-ERS , 573–580. Heidelberg: Springer.
  • Hamada, M. , and C. F. J. Wu . 1990. A critical look at accumulation analysis and related methods. Technometrics 32 (2):119–30. doi: 10.1080/00401706.1990.10484625.
  • Hastie, T. , R. Tibshirani , and J. Friedman . 2009. The elements of statistical learning: Prediction, inference and data mining . New York: Springer-Verlag.
  • Hosmer, D. W. , and S. Lemeshow . 2000. Applied logistic regression . 2nd ed. New York: John Wiley & Sons.
  • Hsieh, K. L. 2011. Quality analysis of a categorical response with different important considerations. Quality & Quantity 45 (2):293–303. doi: 10.1007/s11135-009-9294-z.
  • ISO 105‐A02 . 1993. Textiles – Tests for colour fastness – Part A02: Grey scale for assessing change in colour . Geneva: International Organization for Standardization.
  • Jeng, Y. C. , and S. M. Guo . 1996. Quality improvement for RC06 chip resistor. Quality and Reliability Engineering International 12 (6):439–45. doi: 10.1002/(SICI)1099-1638(199611)12:6<439::AID-QRE61>3.0.CO;2-0.
  • Jeong, I. , and K. Kim . 2009. An interactive desirability function method to multiresponse optimization. European Journal of Operational Research 195 (2):412–26. doi: 10.1016/j.ejor.2008.02.018.
  • Jinks, J. 1987. Reduction of voids in a urethane-foam product. In Fifth Symposium on Taguchi Methods , 135–48. Dearborn, MI: American Suppliers Institute.
  • Köksoy, O. 2005. Dual response optimization: The desirability approach. International Journal of Industrial Engineering: Theory, Applications and Practice 12 (4):335–42.
  • Köksoy, O. , and N. Doganaksoy . 2003. Joint optimization of mean and standard deviation using response surface methods. Journal of Quality Technology 35 (3):239–52. doi: 10.1080/00224065.2003.11980218.
  • Kvålseth, T. O. 1995. Comment on the coefficient of ordinal variation. Perceptual and Motor Skills 81 (2):621–2. doi: 10.1177/003151259508100251.
  • Kvålseth, T. O. 2011. Variation for categorical variables. In International encyclopedia of statistical science , ed. M. Lovric , 1642–5. Berlin, Heidelberg: Springer.
  • Lee, D.-H. , I.-J. Jeong , and K.-J. Kim . 2009. A posterior preference articulation approach to dual-response-surface optimization. IIE Transactions 42 (2):161–71. doi: 10.1080/07408170903228959.
  • Lee, D.-H. , I.-J. Jeong , and K.-J. Kim . 2018. A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach. Quality and Reliability Engineering International 34 (3):360–76. doi: 10.1002/qre.2258.
  • Lee, D.-H. , and K.-J. Kim . 2012. Interactive weighting of bias and variance in dual response surface optimization. Expert Systems with Applications 39 (5):5900–6. doi: 10.1016/j.eswa.2011.11.114.
  • Lee, D.-H. , K.-J. Kim , and M. Köksalan . 2011. A posterior preference articulation approach to multiresponse surface optimization. European Journal of Operational Research 210 (2):301–9. doi: 10.1016/j.ejor.2010.09.032.
  • Lee, D.-H. , K.-J. Kim , and M. Köksalan . 2012. An interactive method to multiresponse surface optimization based on pairwise comparisons. IIE Transactions 44 (1):13–26. doi: 10.1080/0740817X.2011.564604.
  • Lee, H. , and D. H. Lee . 2016. A solution selection approach to multiresponse surface optimization based on a clustering method. Quality Engineering 28 (4):388–401. doi: 10.1080/08982112.2016.1155222.
  • Leik, R. K. 1966. A measure of ordinal consensus. The Pacific Sociological Review 9 (2):85–90. doi: 10.2307/1388242.
  • Lin, D. , and W. Tu . 1995. Dual response surface optimization. Journal of Quality Technology 27 (1):34–9. doi: 10.1080/00224065.1995.11979556.
  • Menard, S. 2010. Logistic regression: From introductory to advances concept and applications . New York: Sage Publications.
  • Miettinen, K. 2008. Introduction to multiobjective optimization: Noninteractive approaches. In Multiobjective optimization: Interactive and evolutionary approaches, lecture notes in computer science , eds. J. Branke , K. Deb , K. Miettinen , and R. Slowiński , vol. 5252, 1–26. Berlin: Springer.
  • Miettinen, K. , F. Ruiz , and A. P. Wierzbicki . 2008. Introduction to multiobjective optimization: Interactive approaches. In Multiobjective optimization: Interactive and evolutionary approaches, lecture notes in computer science , eds. J. Branke , K. Deb , K. Miettinen , and R. Slowiński , vol. 5252, 27–57. Berlin: Springer.
  • Minitab 16 Statistical Software. 2010. State College, PA: Minitab, Inc.
  • Montgomery, D. C. , G. C. Runger , and N. F. Hubele . 2012. Engineering statistics . 5th ed. New York: John Wiley & Sons.
  • Myers, W. R. , W. A. Brenneman , and R. H. Myers . 2005. A dual-response approach to robust parameter design for a generalized linear model. Journal of Quality Technology 37 (2):130–8. doi: 10.1080/00224065.2005.11980311.
  • Myers, R. H. , A. I. Khuri , and G. Vining . 1992. Response surface alternatives to the Taguchi robust parameter design approach. The American Statistician 46 (2):131–9.
  • Myers, R. H. , D. C. Montgomery , G. G. Vining , C. M. Borror , and S. M. Kowalski . 2004. Response surface methodology: A retrospective and literature survey. Journal of Quality Technology 36 (1):53–77. doi: 10.1080/00224065.2004.11980252.
  • Nair, V. N. 1986. Testing in industrial experiments with ordered categorical data. Technometrics 28 (4):283–91. doi: 10.2307/1268974.
  • Ozdemir, A. , and B. R. Cho . 2016. A nonlinear integer programming approach to solving the robust parameter design optimization problem. Quality and Reliability Engineering International 32 (8):2859–70. doi: 10.1002/qre.1970.
  • Park, K. , and K. Kim . 2005. Optimizing multi-response surface problems: How to use multi-objective optimization techniques. IIE Transactions 37 (6):523–32. doi: 10.1080/07408170590928992.
  • Park, G. J. , T. H. Lee , K. H. Lee , and K. H. Hwang . 2006. Robust design: An overview. AIAA Journal 44 (1):181–91. doi: 10.2514/1.13639.
  • R Core Team . 2017. R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing.
  • Robinson, T. J. , C. M. Borror , and R. H. Myers . 2004. Robust parameter design: A review. Quality and Reliability Engineering International 20 (1):81–101. doi: 10.1002/qre.602.
  • Taguchi, G. 1974. A new statistical analysis for clinical data, the accumulating analysis, in contrast with the Chi-Square test. Suishin Igaku 29:806–13.
  • Taguchi, G. , and Y. Wu . 1980. Introduction to off-line quality control . Nagoya, Japan: Central Japan Quality Association.
  • Tucker, G. R. , W. H. Woodall , and K. L. Tsui . 2002. A control chart for ordinal data. American Journal of Mathematical and Management Sciences 22 (1–2):31–48. doi: 10.1080/01966324.2002.10737574.
  • Vining, G. G. , and R. H. Myers . 1990. Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology 22 (1):38–45. doi: 10.1080/00224065.1990.11979204.
  • Weiß, C. H. 2020. Distance-based analysis of ordinal data and ordinal time series. Journal of the American Statistical Association 115 (531):1189–200. doi: 10.1080/01621459.2019.1604370.
  • Wu, F. C. , and C. H. Yeh . 2006. A comparative study on optimization methods for experiments with ordered categorical data. Computers & Industrial Engineering 50 (3):220–32. doi: 10.1016/j.cie.2006.04.001.

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