Abstract
The paper gives a new method to identify significant effects in two-level factorial experiments, and compares the new method with the exiting methods using Monte Carlo simulation. The existing methods only perform well under the assumption of effect sparsity, but the new method performs well without effect sparsity.
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The authors would like to thank the referees for their valuable comments and structural suggestions for improving the paper.
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This paper is supported by the National Natural Science Foundation of China (Nos. 11771250, 11371223).
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Li, H., Zhao, Sl. Identifying Significant Effects in Unreplicated Regular Two-level Factorial Experiments. Acta Math. Appl. Sin. Engl. Ser. 36, 816–824 (2020). https://doi.org/10.1007/s10255-020-0981-9
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DOI: https://doi.org/10.1007/s10255-020-0981-9