Construction and Analysis for the Optimal Supersaturated Designs with a Large Number of Inert Factors

Abstract

Supersaturated designs (SSDs) refer to those designs in which the run size is much smaller than the main effects to be estimated. They are commonly used to identify a few, but critical, active factors from a large set of potentially active ones, keeping the cost as low as possible. In this regard, the development of new construction and analysis methods has recently seen a rapid increase. In this paper, we provide some methods to construct equi- and mixed-level E(f NOD) optimal SSDs with a large number of inert factors using the substitution method. The proposed methods are easy to implement, and many new SSDs can then be constructed from them. We also study a variable selection method based on the screening-selection network (SSnet) method for regression problems. A real example is analyzed to illustrate that it is able to effectively identify active factors. Eight different analysis methods are used to analyze the data generated from the proposed designs. Three scenarios with different setups of parameters are designed, and the performances of each method are illustrated by extensive simulation studies. Among all these methods, SSnet produced the most satisfactory results according to power.