An adaptive resampling test for detecting the presence of significant predictors.
An adaptive resampling test for detecting the presence of significant predictors.
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DOI：
10.1080/01621459.2015.1095099
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发表时间：
2015
影响因子：
3.7
通讯作者：
Qian M
中科院分区：
文献类型：
--
作者：
McKeague IW;Qian M
本文研究了在高维回归中检测显著预测因子存在的边际筛选。由于后模型选择估计量的非标准限制行为，筛选大量的预报器是一个具有挑战性的问题。有一种常见的误解，认为这类估计的Oracle属性是灵丹妙药，但Oracle属性只保留了边际筛选中感兴趣的零假设。为了解决这一困难，我们提出了一种自适应重采样测试(ART)。我们的方法提供了一种替代流行的(但保守的)Bonferroni方法来控制家庭错误率。ART是自适应的，因为阈值被用来决定中心百分位自举是否适用，否则以最紧的方式适应非标准渐近。使用模拟研究评估了该方法的性能，并将其应用于基因表达数据和HIV耐药性数据。
This paper investigates marginal screening for detecting the presence of significant predictors in high-dimensional regression. Screening large numbers of predictors is a challenging problem due to the non-standard limiting behavior of post-model-selected estimators. There is a common misconception that the oracle property for such estimators is a panacea, but the oracle property only holds away from the null hypothesis of interest in marginal screening. To address this difficulty, we propose an adaptive resampling test (ART). Our approach provides an alternative to the popular (yet conservative) Bonferroni method of controlling familywise error rates. ART is adaptive in the sense that thresholding is used to decide whether the centered percentile bootstrap applies, and otherwise adapts to the non-standard asymptotics in the tightest way possible. The performance of the approach is evaluated using a simulation study and applied to gene expression data and HIV drug resistance data.