Analyzing Supersaturated Designs by Means of an Information Based Criterion

Abstract

The cost and time consumption of many industrial experimentations can be reduced using the class of supersaturated designs since this can be used for screening out the important factors from a large set of potentially active variables. A supersaturated design is a design for which there are fewer runs than effects to be estimated. Although there exists a wide study of construction methods for supersaturated designs, their analysis methods are yet in an early research stage. In this article, we propose a method for analyzing data using a correlation-based measure, named as symmetrical uncertainty. This method combines measures from the information theory field and is used as the main idea of variable selection algorithms developed in data mining. In this work, the symmetrical uncertainty is used from another viewpoint in order to determine more directly the important factors. The specific method enables us to use supersaturated designs for analyzing data of generalized linear models for a Bernoulli response. We evaluate our method by using some of the existing supersaturated designs, obtained according to methods proposed by Tang and Wu (1997) as well as by Koukouvinos et al. (2008). The comparison is performed by some simulating experiments and the Type I and Type II error rates are calculated. Additionally, Receiver Operating Characteristics (ROC) curves methodology is applied as an additional statistical tool for performance evaluation.

Keywords:

• Error rates
• Generalized linear models
• Information theory
• ROC
• Screening
• Supersaturated design
• Symmetrical uncertainty

Mathematics Subject Classification:

• 62K15
• 62-07
• 62J12

Acknowledgments

The authors would like to thank the Associate Editor and referees for constructive and useful suggestions that improved the quality of the manuscript.