DIME is a director group of Eurostat - one of the European Commission Directorates-General. The chair of the group comes from Eurostat
Consultation on the extension of the European statistical programme 2013-17 up to 2020
A "World Statistics Café" was organised on 25 November 2014 to gather the viewpoints of users, methodologists and producers of European statistics on the extension of the European statistical programme.
The main objectives for this project are the identification of best practices and the development of common methodology and ESS
The main objectives for this project are the identification of best practices and the development of common methodology and ESS guidelines supporting the production of business statistics aiming at reducing respondent burden and fostering efficiency and integration of processes.
This report presents the results from the first work package within the ESSnet titled Quality of multisource statistics (also known as ESSnet KOMUSO applying an imperfect acronym). The ESSnet
These pages constitute the Handbook on Methodology of Modern Business Statistics; it contains contributions from several European national statistical institutes. The handbook covers all statistical business process steps.
Knottnerus, P. (2016), On New Variance Approximations for Linear Models with Inequality Constraints. Statistica Neerlandica 70, pp. 26-46.
Paper reviewed: Knottnerus , P. and C. v an Duin (2006), Variances in Repeated Weighting with an Application to the Dutch Labour Force Survey. Journal of Official Statistics 22 , pp. 565 – 584.
Di Consiglio L., Tuoto T. (2015). Coverage evaluation on probabilistically linked data, Journal of Offic ial Statistics, Vol. 31, No. 3
Paper reviewed: Fosen, J. and L. - C. Zhang (2011), Quality assessment of register - based census employment status, Proceedings of the International Statistical Institute, World Congress, Dublin.
Administrative data are used more and more in official statistics as a replacement for survey data.
When data sets are linked at individual level, for instance survey data with administrative data, often no unique linkage keys are available. In that case, probabilistic linkage may be used. With probabilistic linkage, linkage errors will occur. These errors may have impact on subsequent statistical analysis.
Schnetzer, M., Astleithner, F., Cetkovic, P., Humer, S., Lenk, M., and Moser, M. (2015), Quality Assessment of Imputations in Administrative Data, Journal of Official Statistics, Vol. 31, No. 2, pp
Estimation of bias and variance of the statistic of interest.