论文标题
对一般替代方案的因子调整的多重测试
A factor-adjusted multiple testing of general alternatives
论文作者
论文摘要
因子调整的多重测试用于处理强相关测试。由于以前的大多数工作都控制了稀疏替代方案下的错误发现率，因此我们为任何真正的错误比例开发了两步方法，即adafat。在本文中，提出的过程通过潜在因子负载调整。在解释变量的存在下，给出了估计因子负荷的均匀收敛速率。我们还表明，ADAFAT的力量与受控的错误发现率一起进行。通过中国A共享市场校准的模拟检查了拟议程序的性能。
Factor-adjusted multiple testing is used for handling strong correlated tests. Since most of previous works control the false discovery rate under sparse alternatives, we develop a two-step method, namely the AdaFAT, for any true false proportion. In this paper, the proposed procedure is adjusted by latent factor loadings. Under the existence of explanatory variables, a uniform convergence rate of the estimated factor loadings is given. We also show that the power of AdaFAT goes to one along with the controlled false discovery rate. The performance of the proposed procedure is examined through simulations calibrated by China A-share market.
