论文标题
通过随机模型的聚集在筛选实验中的选择因子选择
Factor selection in screening experiments by aggregation over random models
论文作者
论文摘要
筛选实验可用于从许多潜在重要因素中筛选出少数真正重要的因素。高斯 - 丹兹格选择器（GDS）通常是用于筛选实验的首选分析方法。仅考虑主效应模型可能会导致错误的结论，但是即使限于两因素相互作用，也包括交互项，也会大大增加模型项的数量，并挑战GDS分析。我们在随机模型（GDS-ARM）上提出了一种新的分析方法，称为Gauss-Dantzig选择器聚集，该方法在多个模型上执行GDS分析，仅包括一些随机选择的相互作用。然后将这些不同分析的结果汇总以确定重要因素。我们讨论了提出的方法，建议选择调整参数，并研究其在真实和模拟数据上的性能。
Screening experiments are useful for screening out a small number of truly important factors from a large number of potentially important factors. The Gauss-Dantzig Selector (GDS) is often the preferred analysis method for screening experiments. Just considering main-effects models can result in erroneous conclusions, but including interaction terms, even if restricted to two-factor interactions, increases the number of model terms dramatically and challenges the GDS analysis. We propose a new analysis method, called Gauss-Dantzig Selector Aggregation over Random Models (GDS-ARM), which performs a GDS analysis on multiple models that include only some randomly selected interactions. Results from these different analyses are then aggregated to identify the important factors. We discuss the proposed method, suggest choices for the tuning parameters, and study its performance on real and simulated data.
