Easy collection and storage of big data-sets, obtained from web based applications, social networks or medical records provide interesting opportunities. Such data sources provide what a statistician needs, that is, millions of observations on thousands of variables. These data-sets however are not collected in any designed way. In other words, they are observational and may not represent the population targeted by the analyst. Use of big data sources with or without an integration with carefully designed survey data would often be beneficial in computing official statistics, which are required for making policy decisions. Integration of various data sources is a popular topic of research in several branches of current statistics.

A workshop on statistical data integration is in programme from 5 to 8 August 2019. The event consists of expository lectures on different aspects of data integration methods in statistics. The broad topics of the lectures includes small area estimation, statistical methods for record linkage, data confidentiality, disclosure methods and privacy assessment, multiple imputation techniques and generation of synthetic data to protect privacy, big data integration techniques in official statistics, methods for analysing big data-sets obtained from social networks, online transactions etc. The workshop is designed to be a precursor to a conference that will take place during the second week (8-16 August), where more recent developments in the above topics would be discussed.

The Invited talk on “A Hierarchical Bayesian Approach for Addressing Multiple Objectives in Poverty Research for Small Areas” takes MAKSWELL’ topics.  It is held by Monica Pratesi (university of Pisa), with Partha Lahiri (University of Maryland) and Gaia Bertarelli, Stefano Marchetti and Nicola Salvati, all members from project’s partner University of Pisa/ Dagum Centre.