Research Group "Stochastic Algorithms and Nonparametric Statistics"
Research Seminar "Mathematical Statistics" Summer Semester 2026
|
|
| 22.04.2026 | Eddie Aamari (École Normale Supérieure, Paris) & Arthur Stéphanovich (ENSAE-CREST, Paris) |
| 3rd part of Mini-Course: "Flow-based generative models: Regularity, stability, and minimax rates" |
|
| 29.04.2026 | Nicola Gnecco (Imperial College London) |
| Extremes of structural causal models The behaviour of extreme observations is well-understood for time series or spatial data, but little is known if the data generating process is a structural causal model (SCM). We study the behavior of extremes in this model class, both for the observational distribution and under extremal interventions. We show that under suitable regularity conditions on the structure functions, the extremal behavior is described by a multivariate Pareto distribution, which can be represented as a new SCM on an extremal graph. Importantly, the latter is a sub-graph of the graph in the original SCM, which means that causal links can disappear in the tails. We further introduce a directed version of extremal graphical models and show that an extremal SCM satisfies the corresponding Markov properties. Based on a new test of extremal conditional independence, we propose two algorithms for learning the extremal causal structure from data. The first is an extremal version of the PC-algorithm, and the second is a pruning algorithm that removes edges from the original graph to consistently recover the extremal graph. The methods are illustrated on river data with known causal ground truth. Organiser: Katarzyna Reluga |
|
| 06.05.2026 | Vladimir Spokoiny (WIAS Berlin) | Estimation of a smooth functional for inverse problems |
| 13.05.2026 | Holger Dette (Ruhr University Bochum) |
| Multiple change point detection in functional data with applications to biomechanical fatigue data Injuries to the lower extremity joints are often debilitating, particularly for professional athletes. Understanding the onset of stressful conditions on these joints is therefore important in order to ensure prevention of injuries as well as individualised training for enhanced athletic performance. We study the biomechanical joint angles from the hip, knee and ankle for runners who are experiencing fatigue. The data is cyclic in nature and densely collected by body worn sensors, which makes it ideal to work with in the functional data analysis (FDA) framework. We develop a new method for multiple change point detection for functional data, which improves the state of the art with respect to at least two novel aspects. First, the curves are compared with respect to their maximum absolute deviation, which leads to a better interpretation of local changes in the functional data compared to classical $L^2$-approaches. Secondly, as slight aberrations are to be often expected in a human movement data, our method will not detect arbitrarily small changes but hunts for relevant changes, where maximum absolute deviation between the curves exceeds a specified threshold, say $\Delta >0$. We recover multiple changes in a long functional time series of biomechanical knee angle data, which are larger than the desired threshold $\Delta$, allowing us to identify changes purely due to fatigue. In this work, we analyse data from both controlled indoor as well as from an uncontrolled outdoor (marathon) setting. |
|
| 20.05.2026 | Johannes Schmidt-Hieber (University of Twente) |
|
|
|
| 27.05.2026 | Alexander Meister (Universität Rostock) |
|
|
|
| 03.06.2026 | |
| |
|
| 10.06.2026 | Michael Sørensen (University of Copenhagen) |
| 17.06.2026 | Anna Calissano (University College London) |
|
|
|
| 24.06.2026 | Bertrand Even (Université Paris-Saclay) |
| |
|
| 01.07.2026 | |
| HVP 11 a, R.313 | |
| 08.07.2026 | Gilles Blanchard (Université Paris Saclay) |
| HVP 11 a, R.313 | |
last reviewed: April 27, 2026 by Christine Schneider