Optimal design subsampling from Big Datasets

Laura Deldossi, Chiara Tommasi

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

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.
Lingua originaleEnglish
pagine (da-a)93-101
Numero di pagine9
RivistaJournal of Quality Technology
Volume54
DOI
Stato di pubblicazionePubblicato - 2022

Keywords

  • design efficiency
  • optimal design theory
  • finite population sampling

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