论文检索

用于在线错误发现率控制的结构自适应序贯检验AI 解读

Consider the online testing of a stream of hypotheses where a real-time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted. This work develops a…

大规模两样本推断中的协变量辅助排序与筛选AI 解读

Summary Two-sample multiple testing has a wide range of applications. The conventional practice first reduces the original observations to a vector of p-values and then chooses a cut-off to adjust for multiplicity. However, this data reduction step could cause significant loss of information and thus lead to suboptimal testing procedures. We introduce a new framework for two-sample multiple testing by incorporating a carefully constructed auxiliary variable in inference to…