- Programme Changes (11.Dec.2018)
- Book of Abstracts (20.Nov.2018)
- Professional meetings (20.Nov.2018)
- List of Participants (09.Oct.2018)
- Programme (09.Oct.2018)
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The 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018) will take place at the University of Pisa, Italy, 14-16 December 2018. Tutorials will be given on Thursday 13th of December 2018 and the CRoNoS Winter Course on Time Series will take place the 11-12 December 2018.
This conference is organized by the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics) and the University of Pisa. The new 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 info@cmstatistics.org or visit the CMStatistics website.
The Conference will take place jointly with the 12th International Conference on Computational and Financial Econometrics (CFE 2018). The conference has a high reputation of quality presentations. The last editions of the joint conference CFE-CMStatistics gathered over 1600 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.