Stability selection has gained popularity as a method for enhancing the performance of variable selection algorithms while controlling false discovery rates. However, achieving these desirable properties depends on correctly specifying the stable threshold parameter, which can be challenging. An arbitrary choice of this parameter can substantially alter the set of selected variables, as the variables' selection probabilities are inherently data-dependent. To address this issue, we propose Exclusion Automatic Threshold Selection (EATS), a data-adaptive algorithm that streamlines stability selection by automating the threshold specification process. EATS initially filters out potential noise variables using an exclusion probability threshold, derived from applying stability selection to a randomly shuffled version of the dataset. Following this, EATS selects the stable threshold parameter using the elbow method, balancing the marginal utility of including additional variables against the risk of selecting superfluous variables. We evaluate our approach through an extensive simulation study, benchmarking across commonly used variable selection algorithms and static stable threshold values.

翻译：稳定性选择作为一种增强变量选择算法性能并控制错误发现率的方法已广受欢迎。然而，实现这些理想特性依赖于正确设定稳定阈值参数，这往往具有挑战性。由于变量的选择概率本质上是数据依赖的，该参数的任意选择可能显著改变所选变量集合。为解决此问题，我们提出排除式自动阈值选择（EATS），一种数据自适应算法，通过自动化阈值设定过程来简化稳定性选择。EATS首先使用排除概率阈值过滤潜在噪声变量，该阈值通过对数据集的随机打乱版本应用稳定性选择获得。随后，EATS采用肘部法则选择稳定阈值参数，在纳入额外变量的边际效用与选择冗余变量的风险之间取得平衡。我们通过广泛的模拟研究评估所提方法，并以常用变量选择算法和静态稳定阈值作为基准进行性能比较。