Supersaturated designs (SSDs) can save considerable cost in industrial experimentation when many potential factors are introduced in preliminary studies. Analyzing data in SSDs is challenging because the number of experiments is less than the number of candidate factors. In this paper, we introduce a variable selection approach to identifying the active effects in SSD via nonconvex penalized least squares. An iterative ridge regression is employed to find the solution of the penalized least squares. We provide both theoretical and empirical justifications for the proposed approach. Some related issues are also discussed.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty