A screening experiment attempts to identify a subset of important effects using a relatively small number of experimental runs. Given the limited run size and a large number of possible effects, penalized regression is a popular tool used to analyze screening designs. In particular, an automated implementation of the Gauss-Dantzig selector has been widely recommended to compare screening design construction methods. Here, we illustrate potential reproducibility issues that arise when comparing screening designs via simulation, and recommend a graphical method, based on screening probabilities, which compares designs by evaluating them along the penalized regression solution path. This method can be implemented using simulation, or, in the case of lasso, by using exact local lasso sign recovery probabilities. Our approach circumvents the need to specify tuning parameters associated with regularization methods, leading to more reliable design comparisons. This article contains supplementary materials including code to implement the proposed methods.

翻译：筛选实验旨在通过相对较少的实验次数来识别重要效应子集。鉴于实验次数有限且可能效应数量庞大，惩罚回归成为分析筛选设计的主流工具。特别地，高斯-丹齐克选择器的自动化实现已被广泛推荐用于比较筛选设计的构造方法。本文阐明了通过模拟比较筛选设计时可能出现的可重复性问题，并提出基于筛选概率的图形方法——该方法沿惩罚回归解路径评估设计。此方法可通过模拟实现，或在线性绝对收缩与选择算子（lasso）情形下，利用精确的局部lasso符号恢复概率实现。我们的方法规避了与正则化方法相关的调参参数指定需求，从而获得更可靠的设计比较。本文的补充材料包含实现所提方法的代码。