[Statlist] Reminder: ETH/UZH Research Seminar by Dominik Rothenhäusler, Stanford University , 07.04.2022

[Kaiser-Heinzmann Susanne]

We are glad to announce the following talk in the ETH/UZH Research Seminar:

„Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty“   

by Dominik Rothenhäusler, Stanford University 

Date and Time: Thursday,  07.04.2022 at 16.15 h
Place: ETH Zurich, HG D 7.1

Abstract: During data analysis, analysts often have to make seemingly arbitrary decisions. For example during data pre-processing, there are a variety of options for dealing with outliers or inferring missing data. Similarly, many specifications and methods can be reasonable to address a certain domain question. This may be seen as a hindrance to reliable inference since conclusions can change depending on the analyst's choices.
In this talk, I argue that this situation is an opportunity to construct confidence intervals that account not only for sampling uncertainty but also some type of distributional uncertainty. Distributional uncertainty is closely related to other issues in data analysis, ranging from dependence between observations to selection bias and confounding. We demonstrate the utility of the approach on simulated and real-world data.
This is joint work with Yujin Jeong.


Seminar website: https://math.ethz.ch/sfs/news-and-events/research-seminar.html


Organisers: A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf