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Generalized linear models for the analysis of taguchi‐type experiments

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  • J. A. Nelder
  • Y. Lee

Abstract

Recent interest in Taguchi's methods have led to developments in joint analysis of the mean and dispersion from designed experiments. A commonly used method is the analysis of variance of the transformed data. However, a single transformation cannot necessarily produce the Normality, constancy of variance and linearity of systematic effects for the mean and dispersion models. We describe the use of generalized linear models for the analysis of such experiments and illustrate the methods with a data set.

Suggested Citation

  • J. A. Nelder & Y. Lee, 1991. "Generalized linear models for the analysis of taguchi‐type experiments," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 7(1), pages 107-120, March.
    • Handle: RePEc:wly:apsmda:v:7:y:1991:i:1:p:107-120
    DOI: 10.1002/asm.3150070110
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    Cited by:

    1. Rossella Berni & Lorenzo Piattoli & Christine Michaela Anderson-Cook & Lu Lu, 2021. "Split-plot designs and multi-response process optimization: a comparison between two approaches," Econometrics Working Papers Archive 2021_17, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    2. Afrânio M.C. Vieira & Roseli A. Leandro & Clarice G.B. Dem�trio & Geert Molenberghs, 2011. "Double generalized linear model for tissue culture proportion data: a Bayesian perspective," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1717-1731, September.
    3. N. R. Parsons & S. G. Gilmour & R. N. Edmondson, 2009. "Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(9), pages 1037-1054.
    4. Stella Cheung, 1996. "Provincial Credit Rating in Canada: An Ordered Probit Analysis," Staff Working Papers 96-6, Bank of Canada.

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