Computer algorithms of lower-order confounding in regular designs

Zhi Li () and Zhiming Li ()
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Zhi Li: Xinjiang University
Zhiming Li: Xinjiang University

Computational Statistics, 2024, vol. 39, issue 2, No 11, 653-676

Abstract: Abstract In the design of experiments, an optimal design should minimize the confounding between factorial effects, especially main effects and two-factor interaction effects. The general minimum lower-order confounding (GMC) criterion can be used to choose optimal regular designs based on the aliased component-number pattern. This paper aims to study the confounding properties of lower-order effects and provide several computer algorithms to calculate the lower-order confounding in regular designs. We provide a search algorithm to obtain GMC designs. Through python software, we conduct these algorithms. Some examples are analyzed to illustrate the effectiveness of the proposed algorithms.

Keywords: Regular design; Aliased component-number pattern; General minimum lower-order confounding criterion; Computer algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01315-3

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