文章基本信息

标题：On the Development of a Local FDR-Based Approach to Testing Two-Way Classified Hypotheses
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作者：Sanat K. Sarkar ; Shinjini Nandi
期刊名称：Sankhya. Series B, applied and interdisciplinary statistics
印刷版ISSN：0976-8386
电子版ISSN：0976-8394
出版年度：2021
卷号：83
页码：1-11
DOI：10.1007/s13571-020-00247-6
语种：English
出版社：Indian Statistical Institute
摘要：Multiple testing of two-way classified hypotheses controlling false discoveries is a commonly encountered statistical problem in modern scientific research. Nevertheless, not much progress has been made yet towards improving existing multiple testing procedures by adequately adjusting them to such structural settings. This paper makes contributions to the development of local false discovery rate (Lfdr) based methodologies under these settings. More specially, it extends the two-component mixture model (Efron et al.J. Am. Statist. Assoc.96, 1151–1160,2001) from un-classified to two-way classified hypotheses, which captures the underlying two-way classification structure of the hypotheses and provides the foundational framework for the development of newer and potentially powerful Lfdr-based multiple testing procedures for the hypotheses.