一篇新的研究论文介绍了一个用于控制科学假设检验中错误发现率（FDR）的框架，特别解决了有限数据和结构化假设空间带来的挑战。所提出的方法通过将现有技术适配到计数空间，实现了精确的FDR控制或最大化统计功效。它还为零分布样本的高效分配提供了见解。 AI
排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了一个新的统计框架。[lever_c_demoted from research: ic=2 ai=0.4]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) …
Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncerta…