- Indications to access the virtual conference (6.Dec.2020)
- Announcement of social events (6.Dec.2020)
- New entry for Twitter notification (25.Nov.2020)
- Updated programme (25.Nov.2020) Follow @CFECMStatistics
The COVID-19 pandemic does not allow to safely hold large in-person events currently. Thus, the 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020) will take place virtually, 19-21 December 2020. Tutorials will also be given virtually on Friday 18th of December 2020.
This conference is organized by the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics), King's Business School, and King's Department of Mathematics. The journal Econometrics and Statistics (EcoSta) and its supplement, the Annals of Computational and Financial Econometrics are the main sponsors of the conference. The journals Econometrics and Statistics and Computational Statistics & Data Analysis will publish selected papers in special peer-reviewed, or regular issues.
Click on the following link if you wish to become a member of CMStatistics. For further information please contact email@example.com or visit the CMStatistics website.
The Conference will take place jointly with the 14th International Conference on Computational and Financial Econometrics (virtual CFE 2020). The conference has a high reputation of quality presentations. The last edition of the joint conference CFE-CMStatistics gathered over 1900 participants.
All topics within the Aims and Scope of the ERCIM Working Group CMStatistics will be considered for oral and poster presentation.
Topics includes, but not limited to: robust methods, statistical algorithms and software, high-dimensional data analysis, statistics for imprecise data, extreme value modeling, quantile regression and semiparametric methods, model validation, functional data analysis, Bayesian methods, optimization heuristics in estimation and modelling, computational econometrics, quantitative finance, statistical signal extraction and filtering, small area estimation, latent variable and structural equation models, mixture models, matrix computations in statistics, time series modeling and computation, optimal design algorithms and computational statistics for clinical research.