基于微阵列数据的Bagging多重比较
Bagging multiple comparisons from microarray data
摘要：
重新研究了大规模同时假设检验问题。为了提高测试的发现能力，提出了Bagging和Subagging程序。该程序在模拟和真实数据中实现。结果表明，装袋和子标记显着提高功率的代价是错误发现率的小幅增加，所提出的“最大对比度”子标记具有超过装袋的边缘，即，产生类似的功率但显著更小的错误发现率。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
