论文标题
在线多重测试的动态算法
Dynamic Algorithms for Online Multiple Testing
论文作者
论文摘要
我们为在线多重测试提供了新的算法，该算法可证明可以控制错误的发现超出（FDX），同时获得比以前的方法更大的功率。通过开发新算法思想的发展可以实现这种统计进步：较早的算法更“静态”，而我们的新算法则可以根据算法积累的财富量进行测试水平的动态调整。我们证明，在各种合成实验中，我们的算法实现了更高的功率。我们还证明，Suplord可以为FDR和FDX提供错误控制，并在停止时间控制FDR。停止时间尤其重要，因为它们允许实验者尽早结束实验，同时保持对FDR的期望控制。据我们所知，Suplord是第一种非平凡算法，可以在在线环境中控制FDR。
We derive new algorithms for online multiple testing that provably control false discovery exceedance (FDX) while achieving orders of magnitude more power than previous methods. This statistical advance is enabled by the development of new algorithmic ideas: earlier algorithms are more "static" while our new ones allow for the dynamical adjustment of testing levels based on the amount of wealth the algorithm has accumulated. We demonstrate that our algorithms achieve higher power in a variety of synthetic experiments. We also prove that SupLORD can provide error control for both FDR and FDX, and controls FDR at stopping times. Stopping times are particularly important as they permit the experimenter to end the experiment arbitrarily early while maintaining desired control of the FDR. SupLORD is the first non-trivial algorithm, to our knowledge, that can control FDR at stopping times in the online setting.
