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Stefan Sperlich
Has grant
Prof.
SwitzerlandResearch Interests
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Recent Grants
Grant: Close
Uniform- and Post-selection inference for Mixed Parameters
Open Date: 2021-02-01
Close Date: 2025-01-31
Grant: Close
EEA-ESEM 2016 (European Economic Association & Econometric Society European Meeting)
Open Date: 2016-09-01
Close Date: 2016-11-30
Grant: Close
Approaches to Robustness of Structured Models in Econometrics: Theory and Applications
Open Date: 2013-01-01
Close Date: 2015-02-28
Positions (1)
Slava Voloshynovskiy
University of Geneva
3 months ago
University of Geneva (Switzerland) is recruiting 9 fully funded PhD positions in the FUNDIS (Foundation Models for Scientific Discovery) project. The positions focus on foundation models , self-supervised learning , world models , explainable AI , bio-inspired AI , and AI for scientific discovery , with applications across astronomy , particle physics , climate science , digital humanities , and global governance . Students will work in an outstanding multidisciplinary research environment with close collaboration across AI and domain sciences, strong publication opportunities, and international scientific collaborations. The post highlights competitive Swiss PhD salaries and co-supervision by an interdisciplinary team of PIs including Slava Voloshynovskiy, François Fleuret, Roland Bouffanais, Daniel Schaerer, Pascal Oesch, Stéphane Paltani, Tobias Golling, Sebastian Engelke, Stefan Sperlich, Béatrice Joyeux-Prunel, and Stas Smirnov. This is a PhD-level opening only. No specific eligibility details such as degree background, GPA, or language requirements are stated in the post. Application deadline: 30 August 2026 .
3 months ago
Articles (3)
Year:
2026
Year:
2026
Direct, indirect and synthetic estimators have a long history in official statistics. While model-based or model-assisted approaches have become very popular, direct and indirect estimators remain the predominant standard and are therefore important tools in practice. This is mainly due to their simplicity, including low data requirements, assumptions and straightforward inference. With the increasing use of domain estimates in policy, the demands on these tools have also increased. Today, they are frequently used for comparative statistics. This requires appropriate tools for simultaneous inference. We study devices for constructing simultaneous confidence intervals and show that simple tools like the Bonferroni correction can easily fail. In contrast, uniform inference based on max-type statistics in combination with bootstrap methods, appropriate for finite populations, work reasonably well. We illustrate our methods with frequently applied estimators of totals and means.
Year:
2024
Collaborators (2)
maria ferrante
Full professor of Economic Statistics
University of Bologna
Juan Manuel Rodriguez-Poo
Professor of Econometrics
Universidad de Cantabria
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