摘要翻译：
重新讨论了大规模同时假设检验的问题。为了提高试验的发现能力，提出了套袋和下陷程序。该程序在模拟和实际数据中都得到了实现。结果表明，在错误发现率略有提高的情况下，套袋和消隐都显著地提高了功率，其中所提出的“最大对比度”消隐比套袋有优势，即产生相似的功率，但错误发现率明显较小。
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英文标题：
《Bagging multiple comparisons from microarray data》
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作者：
Dimitris N. Politis
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  The problem of large-scale simultaneous hypothesis testing is re-visited. Bagging and subagging procedures are put forth with the purpose of improving the discovery power of the tests. The procedures are implemented in both simulated and real data. It is shown that bagging and subagging significantly improve power at the cost of a small increase in false discovery rate with the proposed `maximum contrast' subagging having an edge over bagging, i.e., yielding similar power but significantly smaller false discovery rates.
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PDF链接：
https://arxiv.org/pdf/705.2214