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.

Deldossi, L., Tommasi, C., Optimal design subsampling from Big Datasets, <<JOURNAL OF QUALITY TECHNOLOGY>>, 2022; 54 (1): 93-101. [doi:10.1080/00224065.2021.1889418] [http://hdl.handle.net/10807/202952]

Optimal design subsampling from Big Datasets

Deldossi, L.
Primo
;
2022

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.
2022
Inglese
Deldossi, L., Tommasi, C., Optimal design subsampling from Big Datasets, <<JOURNAL OF QUALITY TECHNOLOGY>>, 2022; 54 (1): 93-101. [doi:10.1080/00224065.2021.1889418] [http://hdl.handle.net/10807/202952]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/202952
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 7
social impact