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An information theoretical algorithm for analyzing supersaturated designs for a binary response

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

Abstract

A supersaturated design is a factorial design in which the number of effects to be estimated is greater than the number of runs. It is used in many experiments, for screening purpose, i.e., for studying a large number of factors and identifying the active ones. In this paper, we propose a method for screening out the important factors from a large set of potentially active variables through the symmetrical uncertainty measure combined with the information gain measure. We develop an information theoretical analysis method by using Shannon and some other entropy measures such as Rényi entropy, Havrda–Charvát entropy, and Tsallis entropy, on data and assuming generalized linear models for a Bernoulli response. This method is quite advantageous as it enables us to use supersaturated designs for analyzing data on generalized linear models. Empirical study demonstrates that this method performs well giving low Type I and Type II error rates for any entropy measure we use. Moreover, the proposed method is more efficient when compared to the existing ROC methodology of identifying the significant factors for a dichotomous response in terms of error rates. Copyright Springer-Verlag 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metrik:v:76:y:2013:i:1:p:1-18
    DOI: 10.1007/s00184-011-0373-5
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    References listed on IDEAS

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    1. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
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    Cited by:

    1. 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.

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