Abstract
Supersaturated designs are very useful in investigating a very large num- ber of factors by a very few experimental runs, particularly in the screening ex- periments. The goal is to identify sparse but dominant active factors with low cost. In this paper, a new analysis procedure called the Stepwise Response Rene- ment Screener (SRRS) method is proposed to screen important eects. Unlike the traditional approach that regress factors with the response in every iteration, the response in each iteration of the SRRS is rened from the previous iteration using the selected potentially important factor. Analyses of two real-life experiments us- ing supersaturated designs suggest that the SRRS method is able to retrieve simialr results as the existing methods do. Simulation studies show that compared to ex- isting methods in the literature, the SRRS method performs well in terms of the true model identication rate and the average model size.
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