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Statistics & Data Science Seminar
"Bringing closure to FDR control: a general principle for multiple testing"
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Speaker: Aaditya Ramdas (CMU) Associate Professor of Statistics and Machine Learning Carnegie Mellon University Monday, September 29, 2025 3:30PM - 5:00PM Teatime at 3:30pm in 1307
Talk at 4:00pm in 1327 Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=48f0017a-a840-4712-9e79-b34c01045f63 |
Abstract: Since the publication of the seminal Benjamini-Hochberg paper (the most cited paper in statistics), it has been an open problem how the “closure principle” applies to controlling the false discovery rate (FDR). As background, the closure principle, formulated in a seminal 1976 paper, states that every procedure for controlling the familywise error rate (FWER) can be recovered or improved via “closed testing”.
We fully settle this open problem by finally developing a closure principle not only for FDR, but every error metric that is an expectation (including the classical one for FWER as a special case). Also surprisingly, the new generalized closure principle is formulated using the modern concept of e-values, which perhaps explains why it had not been discovered in the past 30 years despite explicit efforts.
This theoretical advance has immediate implications for practice: it leads to surprising improvements to both modern and classical FDR methods (eg: Benjamini-Yekutieli’s famous 2001 procedure is strictly improved, as is the e-BH procedure), and it also allows for practitioners to choose the error metric post-hoc (and sometimes the error level itself).
https://arxiv.org/abs/2509.02517 is the preprint, joint work with Ziyu Xu, Aldo Solari, Lasse Fischer, Rianne de Heide, Jelle Goeman (it is actually a merge of two simultaneous papers).
Speaker Bio: Aaditya Ramdas is a tenured Associate Professor at Carnegie Mellon University in the Departments of Statistics and Machine Learning. His work has been recognized by the Presidential Early Career Award (PECASE), the highest distinction bestowed by the US government to young scientists, a Kavli fellowship from the NAS, a Sloan fellowship in Mathematics, a CAREER award from the NSF, the inaugural COPSS Emerging Leader Award, the Bernoulli new researcher award and the IMS Peter Hall Early Career Prize, and faculty research awards from Adobe and Google. He was recently elected Fellow of the IMS, was awarded Statistician of the Year by the ASA’s Pittsburgh Chapter, and is the program chair of AISTATS 2026.
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