The 24th International Conference on Computational Statistics will take place at the University of Bologna, Italy, 23-26 August 2022. Tutorials will be given during the conference. The conference is sponsored by the European Regional Section of the IASC, and is hosted and organized by the Department of Statistical Sciences of the University of Bologna.

Due to the COVID-19 pandemic, the conference will be hybrid:

  • Session organizers will choose either the in-person, the hybrid or the virtual option.
  • Invited speakers should coordinate their presentation mode with the session organizers.
  • Contributed speakers can choose the option of in-person or virtual presentation.
  • Session organizers and contributed speakers will be able to change their contribution mode until the 10th of June 2022. After that date, any change is subject to availability.
  • All the keynote talks, the special invited sessions, the hybrid organized sessions, and the virtual sessions will be live-streamed on Zoom for all the conference participants.
  • The Poster Sessions will be virtual.
  • All the participants can attend the conference in person, even if they choose a virtual oral presentation or a poster.

The tutorials, invited, organized and contributed sessions will take place in parallel. An invited session presentation is about 30 mins, while an organized or contributed session presentation is about 20 minutes. Tutorials will last about 100 minutes.

Aims and Scope
The conference aims to bring together researchers and practitioners to discuss recent developments in computational statistics, methodology for data analysis and applications. All topics within the broad interface of Computing & Statistics will be considered for oral and poster presentation. Topics include, but are not limited to:
  • biostatistics & biocomputing
  • categorical data analysis
  • clustering & classification
  • computer-aided data analysis
  • computational Bayesian methods
  • computational econometrics
  • data visualization
  • extreme value theory & applications
  • functional data analysis
  • high-dimensional data analysis
  • kernel & Monte Carlo methods
  • machine learning
  • mixture models
  • multivariate data analysis
  • nonparametric statistics
  • numerical methods in statistics
  • optimization heuristics in statistical modeling
  • parametric & semi parametric models
  • robust statistics
  • sampling methods
  • signal processing
  • spatial statistics
  • symbolic data analysis
  • time series analysis