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Generalized minimum aberration mixed-level orthogonal arrays: A general approach based on sequential integer quadratically constrained quadratic programming

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  • Roberto Fontana

Abstract

Orthogonal fractional factorial designs and in particular orthogonal arrays (OAs) are frequently used in many fields of application, including medicine, engineering, and agriculture. In this article, we present a methodology and an algorithm to find an OA, of given size and strength, which satisfies the generalized minimum aberration criterion. The methodology is based on the joint use of polynomial counting functions, complex coding of levels, and algorithms for quadratic optimization and puts no restriction on the number of levels of each factor.

Suggested Citation

  • Roberto Fontana, 2017. "Generalized minimum aberration mixed-level orthogonal arrays: A general approach based on sequential integer quadratically constrained quadratic programming," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(9), pages 4275-4284, May.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:9:p:4275-4284
    DOI: 10.1080/03610926.2015.1081947
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    Cited by:

    1. Yaquelin Verenice Pantoja-Pacheco & Armando Javier Ríos-Lira & José Antonio Vázquez-López & José Alfredo Jiménez-García & Martha Laura Asato-España & Moisés Tapia-Esquivias, 2021. "One Note for Fractionation and Increase for Mixed-Level Designs When the Levels Are Not Multiple," Mathematics, MDPI, vol. 9(13), pages 1-20, June.
    2. Grömping, Ulrike & Fontana, Roberto, 2019. "An algorithm for generating good mixed level factorial designs," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 101-114.

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