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.

Optimal design subsampling from Big Datasets / L. Deldossi, C. Tommasi. - In: JOURNAL OF QUALITY TECHNOLOGY. - ISSN 0022-4065. - (2021), pp. 1-25. [Epub ahead of print] [10.1080/00224065.2021.1889418]

Optimal design subsampling from Big Datasets

C. Tommasi
Ultimo
2021

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.
design efficiency; finite population sampling; optimal design theory
Settore SECS-S/01 - Statistica
4-mar-2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/839542
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