论文标题
K样本多个假设测试信号检测
K-sample Multiple Hypothesis Testing for Signal Detection
论文作者
论文摘要
本文研究了在嘈杂测量中估计信号发生位置的经典问题。基于多个假设测试方案，我们设计了K-sample统计测试以控制错误的发现率（FDR）。具体而言，我们首先将嘈杂的测量与平滑内核一起卷积，并找到所有局部最大值。然后，我们评估每个局部最大值附近K条目的关节概率，得出相应的p值，然后应用Benjamini-Hochberg程序来说明多重性。我们通过广泛的实验证明，与单样本测试相比，我们提出的使用K = 2的方法控制着规定的FDR，同时增加了功率。
This paper studies the classical problem of estimating the locations of signal occurrences in a noisy measurement. Based on a multiple hypothesis testing scheme, we design a K-sample statistical test to control the false discovery rate (FDR). Specifically, we first convolve the noisy measurement with a smoothing kernel, and find all local maxima. Then, we evaluate the joint probability of K entries in the vicinity of each local maximum, derive the corresponding p-value, and apply the Benjamini-Hochberg procedure to account for multiplicity. We demonstrate through extensive experiments that our proposed method, with K=2, controls the prescribed FDR while increasing the power compared to a one-sample test.
