Changepoint estimation: another look at multiple testing problems
针对局部聚集信号的大规模多重检验问题，利用变点分析技术提出边界检测算法，提升检验精度，适用于基因组关联数据。
We consider large scale multiple testing for data that have locally clustered signals. With this structure, we apply techniques from changepoint analysis and propose a boundary detection algorithm so that the clustering information can be utilized. Consequently the precision of the multiple testing procedure is substantially improved. We study tests with independent as well as dependent $p$-values. Monte Carlo simulations suggest that the methods perform well with realistic sample sizes and show improved detection ability compared with competing methods. Our procedure is applied to a genome-wide association dataset of blood lipids.