High-Dimensional Statistics

Hausdorff Center for Mathematics
26-30 July 2021

Many estimation problems in modern statistics are high-dimensional, that is, the number of parameters to be estimated is much higher than the number of available observations. High-dimensional estimation problems have received a lot of attention in recent years and a wide range of statistical tools have been developed to deal with them. Prominent examples are the Lasso, boosting algorithms, neural networks and their recent reincarnation in deep learning.

The Hausdorff School, which is directed at graduate students and postdocs, will give insight into recent advances in the field of high-dimensional statistics. The courses will cover topics such as theory for high-dimensional linear models, bootstrap methods in high dimensions, neural networks and functional data analysis.

Organizers: Sven Otto (Universität Bonn), Michael Vogt (Universität Ulm)

Key Speakers: The following speakers will give a lecture series:

  •  Kengo Kato (Cornell University, US)
  •  Hannes Leeb (University of Vienna, Austria)
  •  Johannes Schmidt-Hieber (University of Twente, Netherlands)
  •  Jane-Ling Wang (University of California, Davis, US)

Practical

  • 26-30 July 2021
  • location: online of on campus
    Lipschitz-Saal (Endenicher Allee 60, Bonn, Germany)
  • Language: English

Registration

  • register with a CV, letter of intent, and contact of reference
  • Target audience: PhD students

Ready to get started?

All practical information can be found on the website of Hausdorff Center for Mathematics.