Optimal design subsampling from Big Datasets

Laura Deldossi, Chiara Tommasi

Research output: Contribution to journalArticle


Big Data are huge amounts of digital information that rarely result from properly planned surveys; as a consequence they often contain redundant observations. When the aim is to answer particular questions of interest, we suggest selecting a subsample of units that contains the majority of the information to achieve this goal. Selection methods driven by the theory of optimal design incorporate the inferential purposes and thus perform better than standard sampling schemes.
Original languageEnglish
Pages (from-to)93-101
Number of pages9
JournalJournal of Quality Technology
Publication statusPublished - 2022


  • design efficiency
  • finite population sampling
  • optimal design theory


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