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EnglishBig 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.
Deldossi, L., Tommasi, C., Optimal Design of Experiments and Model-based survey sampling in Big-Data, in Programme and Abstracts, 19th Annual ENBIS Conference, Budapest, 2-4 september 2019, (Budapest (Ungheria), 02-04 September 2019), Jens Bischoff, Agnes Backhausz, Rossella Berni, Sonja Kuhnt, Lluıs Marco- Almagro, Antonio Pievatolo, Marco P. Seabra dos Reis and Murat Caner Testik, Budapest, Hungary 2019:2019 37-37 [http://hdl.handle.net/10807/147206]
| Autori: | ||
| Titolo: | Optimal Design of Experiments and Model-based survey sampling in Big-Data | |
| Data di pubblicazione: | 2019 | |
| Abstract: | 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. | |
| Lingua: | Inglese | |
| Titolo del libro: | Programme and Abstracts, 19th Annual ENBIS Conference, Budapest, 2-4 september 2019 | |
| ISBN: | 9789634891468 | |
| Editore: | Jens Bischoff, Agnes Backhausz, Rossella Berni, Sonja Kuhnt, Lluıs Marco- Almagro, Antonio Pievatolo, Marco P. Seabra dos Reis and Murat Caner Testik | |
| Nome del convegno: | 19th Annual ENBIS Conference | |
| Luogo del convegno: | Budapest (Ungheria) | |
| Data inizio evento: | 2019-09-02 | |
| Data fine evento: | 2019-09-04 | |
| Citazione: | Deldossi, L., Tommasi, C., Optimal Design of Experiments and Model-based survey sampling in Big-Data, in Programme and Abstracts, 19th Annual ENBIS Conference, Budapest, 2-4 september 2019, (Budapest (Ungheria), 02-04 September 2019), Jens Bischoff, Agnes Backhausz, Rossella Berni, Sonja Kuhnt, Lluıs Marco- Almagro, Antonio Pievatolo, Marco P. Seabra dos Reis and Murat Caner Testik, Budapest, Hungary 2019:2019 37-37 [http://hdl.handle.net/10807/147206] | |
| Appare nelle tipologie: | Atti di Convegno, Congresso, Giornate di studio, ecc., Workshop (in volume) |
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