The working group (WG) CMStatistics focuses on all computational and methodological aspects of statistics. Of particular interest is research in important statistical applications areas where both computational and/or methodological aspects have a major impact. The aim is threefold: first, to consolidate the research in computational and methodological statistics that is scattered throughout Europe; second, to provide researchers with a network from which they can obtain an unrivalled sources of information about the most recent developments in computational and methodological statistics as well as its applications; third to edit quality publications of high impact and significance in the broad interface of computing, methodological statistics and its applications.

The Elsevier journals Computational Statistics and Data Analysis (CSDA) and Econometrics and Statistics (EcoSta) are the official journals of the CMStatistics network. The CMStatistics also publishes the Annals of Statistical Data Science as a supplement to the CSDA.

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Organization and Activities
The WG comprises a number of specialized teams in various research areas of computational and methodological statistics. The teams act autonomously within the framework of the WG in order to promote their own research agenda. Their activities are endorsed by the WG. They submit research proposals, organize sessions, tracks and tutorials during the annual WG meetings and edit the journal Econometrics and Statistics (EcoSta) and special issues of the journals Computational Statistics & Data Analysis (CSDA) and EcoSta.
The specialized teams are listed here: Specialized Teams.
The scope of the WG is broad enough to include members in all areas of methododological statistics and those of computing that have an impact on statistical techniques. Applications of statistics in diverse disciplines are strongly represented. These areas include economics, medicine, epidemiology, biology, finance, physics, chemistry, climatology and communication. The range of topics addressed and the depth of coverage establish the WG as an essential research network in the interdisciplinary area of advanced computational and methodological statistics.