Online testing procedures aim to control the extent of false discoveries over a sequence of hypothesis tests, allowing for the possibility that early-stage test results influence the choice of hypotheses to be tested in later stages. Typically, online methods assume that a permanent decision regarding the current test (reject or not reject) must be made before advancing to the next test. We instead assume that each hypothesis requires an immediate preliminary decision, but also allows us to update that decision until a preset deadline. Roughly speaking, this lets us apply a Benjamini-Hochberg-type procedure over a moving window of hypotheses, where the threshold parameters for upcoming tests can be determined based on preliminary results. Our method controls the false discovery rate (FDR) at every stage of testing, as well as at adaptively chosen stopping times. These results apply even under arbitrary p-value dependency structures.
翻译:在线测试程序旨在控制对一系列假设测试的虚假发现程度,允许早期测试结果影响后期测试的假设选择的可能性。 通常, 在线方法假定在推进下一个测试之前必须就当前测试做出永久决定( 拒绝或不拒绝 ) 。 我们相反地假设每个假设都需要立即做出初步决定, 但也允许我们更新该决定, 直到预先设定的最后期限。 粗略地说, 这让我们对一个移动的假设窗口应用本杰明- 霍奇伯格式程序, 该窗口的即将进行的测试的临界参数可以基于初步结果来确定。 我们的方法控制每个测试阶段的虚假发现率( FDR ), 以及适应性选择的停止时间。 这些结果即使在武断的p- 价值依赖结构下也适用。