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Analyzing supersaturated designs for discrete responses via generalized linear models

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  • N. Balakrishnan
  • C. Koukouvinos
  • C. Parpoula

Abstract

A supersaturated design is a factorial design in which the number of factors to be estimated is larger than the available number of experimental runs. The cost and time required for many industrial experimentations can be reduced by using the class of supersaturated designs, since the main goal for such a design is to identify only a few of the factors under consideration that have dominant effects and to do this identification at a minimal cost. While most of the literature on supersaturated designs has focused on the construction of designs and their optimality properties, the data analysis of such designs has not been developed to a great extent. In this paper, we propose a supersaturated design analysis method, by assuming generalized linear models for discrete responses, for analyzing main effects designs and identifying simultaneously the effects that are significant. Empirical study demonstrates that this method performs well with low Type I and Type II error rates. The proposed method is therefore useful as it enables us to use supersaturated designs for analyzing data on discrete response regression models. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • N. Balakrishnan & C. Koukouvinos & C. Parpoula, 2015. "Analyzing supersaturated designs for discrete responses via generalized linear models," Statistical Papers, Springer, vol. 56(1), pages 121-145, February.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:1:p:121-145
    DOI: 10.1007/s00362-013-0569-z
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    References listed on IDEAS

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    1. N. Balakrishnan & C. Koukouvinos & C. Parpoula, 2013. "An information theoretical algorithm for analyzing supersaturated designs for a binary response," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 1-18, January.
    2. Hans Pettersson, 2005. "Optimal design in average for inference in generalized linear models," Statistical Papers, Springer, vol. 46(1), pages 79-99, January.
    3. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
    4. Marley, Christopher J. & Woods, David C., 2010. "A comparison of design and model selection methods for supersaturated experiments," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3158-3167, December.
    5. Chong Hong & Beom Kim, 2011. "Mutual information and redundancy for categorical data," Statistical Papers, Springer, vol. 52(1), pages 17-31, February.
    6. Claudia Czado & Adrian Raftery, 2006. "Choosing the link function and accounting for link uncertainty in generalized linear models using Bayes factors," Statistical Papers, Springer, vol. 47(3), pages 419-442, June.
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