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A screening experiment attempts to identify a subset of important effectsusing a relatively small number of experimental runs. Given the limited runsize and a large number of possible effects, penalized regression is a populartool used to analyze screening designs. In particular, an automatedimplementation of the Gauss-Dantzig selector has been widely recommended tocompare screening design construction methods. Here, we illustrate potentialreproducibility issues that arise when comparing screening designs viasimulation, and recommend a graphical method, based on screening probabilities,which compares designs by evaluating them along the penalized regressionsolution path. This method can be implemented using simulation, or, in the caseof lasso, by using exact local lasso sign recovery probabilities. Our approachcircumvents the need to specify tuning parameters associated withregularization methods, leading to more reliable design comparisons. Thisarticle contains supplementary materials including code to implement theproposed methods.

A Graphical Comparison of Screening Designs using Support Recovery Probabilities