Dynamic Data Science and Official Statistics


  • Author: Mary E. Thompson, University of Waterloo
  • Date: 13 July 2018
  • Copyright: image appears courtesy of Getty Images

With its emphasis on data quality and supportable results, the practice of Official Statistics faces a variety of statistical and data science issues. A paper recently published in The Canadian Journal of Statistics discusses the importance of population frames and their maintenance; the potential for use of multi‐frame methods and linkages; how the use of large scale non‐survey data may shape the objects of inference; the complexity of models for large data sets; the importance of recursive methods and regularization; and the benefits of sophisticated spatial visualization tools in capturing spatial variation and temporal change.

The author explains her findings further below and the article is available here:

thumbnail image: Dynamic Data Science and Official Statistics

Many of the challenges and opportunities of data science have to do with dynamic aspects: a growing volume of administrative and commercial data on individuals and establishments, continuous flows of data and the capacity to analyze and summarize them in real time, and the necessity for resources to maintain and manage them. Official Statistics as an enterprise provides a moving portrait of the state and the world, encompassing population, economy, health, environment -- and society itself. In this dynamic context, the practice of Official Statistics faces a variety of statistical and data science issues, consistent with its necessary emphases on data quality, privacy and security, and supportable results.

Dynamic Data Science and Official Statistics

Canadian Journal of Statistics, Volume 46, Issue 1, March 2018, pp. 10-23

Special Issue on Big Data and the Statistical Sciences

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