报告题目：Large-scale multiple testing without p-values
报告时间：5月17日，星期二，上午10：00—12：00
报告方式：线上
腾讯会议号：184-163-410
报告摘要：
In this talk, I will introduce our recent works about large-scale multiple testing without p-values. Large-scale multiple testing is a common and important problem, with extensive and profound applications in many disciplines. False discovery rate (FDR) is a key concept to maintain the ability to reliably detect true alternatives without excessive false positive results. Currently most FDR control methods are based on p-values. However, in many situations, p-value is not readily available or accurate. For these problems, we propose a very simple and effective solution. The core idea is to use sample splitting to construct a series of symmetric statistics, and utilize the symmetry of statistics to approximate the number of false discoveries. I will illustrate the basic idea through several important examples. For different problems, I will discuss the construction of symmetric statistics and establish corresponding theoretical results. Numerical studies show that our proposed methods are promising alternatives when p-values are not easy to obtain.
