International Seminar on Selective Inference
A weekly online seminar on selective inference, multiple testing, and post-selection inference.
Gratefully inspired by the Online Causal Inference Seminar
Mailing List
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Upcoming Seminar Presentations
All seminars take place Mondays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 6:30 pm Tel Aviv. Past seminar presentations are posted here.
Monday, December 9, 2024 [link to join]
Speaker: Muriel Pérez-Ortiz (Eindhoven University of Technology)
Title: E-statistics, group invariance and anytime-valid testing
Abstract: We study worst-case-growth-rate-optimal (GROW) e-statistics for hypothesis testing between two group models. It is known that under a mild condition on the action of the underlying group G on the data, there exists a maximally invariant statistic. We show that among all e-statistics, invariant or not, the likelihood ratio of the maximally invariant statistic is GROW, both in the absolute and in the relative sense, and that an anytime-valid test can be based on it. The GROW e-statistic is equal to a Bayes factor with a right Haar prior on G. Our treatment avoids nonuniqueness issues that sometimes arise for such priors in Bayesian contexts. A crucial assumption on the group G is its amenability, a well-known group-theoretical condition, which holds, for instance, in scale-location families. Our results also apply to finite-dimensional linear regression.
Discussant: Junu Lee (University of Pennsylvania)
Links: [Relevant papers: paper #1]
Format
The seminars are held on Zoom and last 60 minutes:
45 minutes of presentation
15 minutes of discussion, led by an invited discussant
Moderators collect questions using the Q&A feature during the seminar.
How to join
You can attend by clicking the link to join (there is no need to register in advance).
More instructions for attendees can be found here.
Organizers
Will Fithian (UC Berkeley)
Jelle Goeman (Leiden University)
Nikos Ignatiadis (University of Chicago)
Lihua Lei (Stanford University)
Zhimei Ren (University of Pennsylvania)
Former organizers
Rina Barber (University of Chicago)
Daniel Yekutieli (Tel Aviv University)
Contact us
If you have feedback or suggestions or want to propose a speaker, please e-mail us at selectiveinferenceseminar@gmail.com.
What is selective inference?
Broadly construed, selective inference means searching for interesting patterns in data, usually with inferential guarantees that account for the search process. It encompasses:
Multiple testing: testing many hypotheses at once (and paying disproportionate attention to rejections)
Post-selection inference: examining the data to decide what question to ask, or what model to use, then carrying out one or more appropriate inferences
Adaptive / interactive inference: sequentially asking one question after another of the same data set, where each question is informed by the answers to preceding questions
Cheating: cherry-picking, double dipping, data snooping, data dredging, p-hacking, HARKing, and other low-down dirty rotten tricks; basically any of the above, but done wrong!