使用支持恢复概率对筛选设计进行图形化比较
A Graphical Comparison of Screening Designs using Support Recovery Probabilities
AI总结：
针对筛选设计模拟比较中的可重复性问题，提出基于筛选概率的图形化方法，通过沿惩罚回归求解路径评估设计，规避调整参数，实现更可靠的设计比较。
摘要：
筛选实验试图使用相对较少的实验运行次数来识别重要效应的子集。鉴于有限的运行次数和大量可能的效应，惩罚回归是用于分析筛选设计的流行工具。特别是，Gauss-Dantzig选择器的自动化实现被广泛推荐用于比较筛选设计构建方法。在这里，我们说明了通过模拟比较筛选设计时出现的潜在可重复性问题，并推荐了一种基于筛选概率的图形化方法，该方法通过沿着惩罚回归求解路径评估设计来比较它们。该方法可以使用模拟来实现，或者在lasso的情况下，通过使用精确的局部lasso符号恢复概率来实现。我们的方法规避了指定与正则化方法相关的调整参数的需要，从而实现更可靠的设计比较。本文包含补充材料，包括实现所提方法的代码。
英文摘要：
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.
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