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Statistica Sinica 32 (2022), 825-845

A CLASS OF MULTILEVEL NONREGULAR DESIGNS
FOR STUDYING QUANTITATIVE FACTORS

Lin Wang and Hongquan Xu

The George Washington University and University of California, Los Angeles

Abstract: Fractional factorial designs are widely used to design screening experiments. Nonregular fractional factorial designs can have better properties than regular designs, but their construction is challenging. Current research on the construction of nonregular designs focuses on two-level designs. We provide a novel class of multilevel nonregular designs by permuting levels of regular designs. We develop a theory illustrating how levels can be permuted without a computer search and, accordingly, propose a sequential method for constructing nonregular designs. Compared with regular designs, these nonregular designs provide more accurate estimations on factorial effects and more efficient screening for experiments with quantitative factors. We further explore the space-filling property of the obtained designs and demonstrate their superiority.

Key words and phrases: Generalized minimum aberration, geometric isomorphism, level permutation, orthogonal array, regular design, Williams transformation.

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