文章基本信息

标题：Testing for dependence on tree structures
本地全文：下载
作者：Merle Behr ; M. Azim Ansari ; Axel Munk 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：18
页码：9787-9792
DOI：10.1073/pnas.1912957117
出版社：The National Academy of Sciences of the United States of America
摘要：Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.
关键词：subgroup detection ; hypothesis testing ; tree structures ; change-point detection