A Method for Analyzing Supersaturated Designs with a Block Orthogonal Structure

Abstract

Supersaturated designs is a large class of factorial designs which can be used for screening out the important factors from a large set of potentially active variables. The huge advantage of these designs is that they reduce the experimental cost drastically, but their critical disadvantage is the confounding involved in the statistical analysis. In this article, we propose a method for analyzing data using a specific type of supersaturated designs. This method heavily uses the special block orthogonal structure of the supersaturated designs given by Tang and Wu (1997). Also, we compare our method with several known statistical analysis methods by using some of the existing supersaturated designs. The comparison is performed by some simulating experiments and the Type I and Type II error rates are calculated. The results are presented in tables and the discussion to follow.

Keywords:

• Data analysis
• Penalized least squares
• Supersaturated design

Mathematics Subject Classification:

• 62K15, 62-07

Acknowledgments

The authors would like to thank Professors Dennis K. J. Lin and Richard Runze Li for sending the Matlab code for the procedures proposed in their article. This made the comparison easier and save us a lot of time. We would like to thank the anonymous referees for their detailed comments and suggestions. The research of the second author is financially supported by Greek State Scholarships Foundation (IKY).