Optimal Design of Experiments and Model-based survey sampling in Big-Data

Laura Deldossi, C. Tommasi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Big Data are generally huge quantities of digital information accrued automatically and/or merged from several sources and rarely result from properly planned population surveys. A Big Dataset is herein conceived as a collection of information concerning a nite population. Since the anal- ysis of an entire Big Dataset can require enormous computational eort, we suggest selecting a sample of observations and using this sampling information to achieve the inferential goal. Instead of the design-based survey sampling approach (which relates to the estimation of summary nite population measures, such as means, totals, proportions) we con- sider the model-based sampling approach, which involves inference about parameters of a super-population model. This model is assumed to have generated the nite population values, i.e. the Big Dataset. Given a super-population model we can apply the theory of optimal design to draw a sample from the Big Dataset which contains the majority of in- formation about the unknown parameters of interest. In addition, since a Big Dataset might provide poor information despite its size, from the def- inition of eciency of a design we suggest a device to measure the quality of the Big Data.
Original languageEnglish
Title of host publicationProgramme and Abstracts, 19th Annual ENBIS Conference, Budapest, 2-4 september 2019
Number of pages1
Publication statusPublished - 2019
Event19th Annual ENBIS Conference - Budapest (Ungheria)
Duration: 2 Sept 20194 Sept 2019


Conference19th Annual ENBIS Conference
CityBudapest (Ungheria)


  • Business and Industrial Statistics
  • European Network


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