Changepoint estimation: another look at multiple testing problems

被引：19

作者：

Cao, Hongyuan ^{[1
]}

Wu, Wei Biao ^{[2
]}

机构：

[1] Univ Missouri, Dept Stat, Columbia, MO 65211 USA

[2] Univ Chicago, Dept Stat, Chicago, IL 60637 USA

来源：

BIOMETRIKA | 2015年 / 102卷 / 04期

基金：

美国国家科学基金会;

关键词：

Changepoint detection; Multiple hypothesis testing; p-value aggregation; BINARY SEGMENTATION; DISCOVERY;

D O I：

10.1093/biomet/asv031

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：974 / 980

页数：7

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