数据自适应自动阈值校准用于稳定性选择
Data-Adaptive Automatic Threshold Calibration for Stability Selection
AI总结：
本文提出EATS算法，通过数据自适应方法自动校准稳定性选择的阈值参数，以提高变量选择的稳定性和准确性。
摘要：
稳定性选择作为一种增强变量选择算法性能并控制误发现率的方法，已受到广泛关注。然而，实现这些理想特性依赖于正确指定稳定的阈值参数，这可能具有挑战性。对这个参数的任意选择会显著改变所选变量的集合，因为变量的选择概率本质上是数据依赖的。为了解决这个问题，我们提出了一种名为排除自动阈值选择（EATS）的数据自适应算法，它通过自动化阈值指定过程来简化稳定性选择。EATS首先使用排除概率阈值过滤掉潜在的噪声变量，该阈值是通过将稳定性选择应用于随机洗牌后的数据集来获得的。随后，EATS使用肘部方法选择稳定的阈值参数，平衡包括额外变量的边际效用与选择冗余变量的风险。我们通过广泛的模拟研究评估了我们的方法，跨常见的变量选择算法和静态稳定阈值值进行基准测试。
英文摘要：
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.
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