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CMStatistics 2017 (ERCIM 2017)

The 10th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2017) will take place at the Senate House, University of London, UK, 16-18 December 2017. Tutorials will be given on Friday 15th of December 2017 and the CRoNoS Winter Course on Copula-based modelling with R will take place the 13-14 December 2017.

This conference is organized by the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics), Birkbeck University of London and King's College London. 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 publishes 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 or visit the CMStatistics website.

The Conference will take place jointly with the 11th International Conference on Computational and Financial Econometrics (CFE 2017). The conference has a high reputation of quality presentations. The last editions of the joint conference CFE-CMStatistics gathered over 1500 participants.

Aims and Scope

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.