YoungStatS
The blog of Young Statisticians Europe (YSE)
webinars
Extrapolation to unseen domains: from theory to applications
2024-04-05
Extrapolation to unseen domains: from theory to applications Monday, April 22nd, 2024, 8:00 PT / 11:00 ET / 17:00 CET 3rd joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: […] Speakers: […]…
bayesian-statistics
Linear-cost unbiased estimator for large crossed random effect models via couplings
Paolo Ceriani and Giacomo Zanella
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2023-09-27
In the following we show how it is possible to obtain parallelizable, unbiased and computationally cheap estimates of Crossed random effects models with a linear cost in the number of datapoints (and paramaters) exploiting couplings. […] CREM model a continuous response variables \(Y\) as…
webinars
Algorithmic Fairness
2023-09-19
Algorithmic Fairness Tuesday, October 3rd, 2023, 7:30 PT / 10:30 ET / 16:30 CET 2nd joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: […] Speakers: […] Abstract: Multi-calibration is a powerful and…
webinars
Distribution generalization and causal inference
2023-02-28
Distribution generalization and causal inference Monday, March 20th, 2023, 7:00 PT / 10:00 ET / 15:00 CET 1st joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: […] Speakers: […] Abstract:…
machine-learning
Inference on Adaptively Collected Data
Ruohan Zhan
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2022-10-11
It is increasingly common for data to be collected adaptively, where experimental costs are reduced progressively by assigning promising treatments more frequently. However, adaptivity also poses great challenges on post-experiment inference, since observations are dependent, and standard estimates…
optimal-transport
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport
Tin Lok James Ng
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2022-09-19
A NHPP defined on \({\cal S} \subset \mathbb{R}^{d}\) can be fully characterized through its intensity function \(\lambda: {\cal S} \rightarrow [0, \infty)\). We present a general model for the intensity function of a non-homogeneous Poisson process using measure transport. The model finds its roots…
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